Relationship and credibility based experience rating and skill discovery system

ABSTRACT

An experience rating and skill discovery system (ERSDS) and a method for determining credibility of experience ratings provided by one or more reviewers and discovering skills of opportunity seekers based on a relationship between the reviewers and the opportunity seekers are provided. A skill profile module of the ERSDS reads profile data from a user profile list and generates a skills profile list. An invitation module transmits invitations to reviewer devices for providing the experience ratings. An aggregation module aggregates the experience ratings and generates an aggregated experience credibility measure and an aggregated experience plausibility measure, and further an aggregated skill amount measure and an aggregated skill credibility measure corresponding to the experience summaries in an experience summary list and the skills of the opportunity seeker respectively, using a relationship depth computed from the relationship data and the experience ratings received by a user association module and a rating module respectively.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of the non-provisionalpatent application Ser. No. 16/507,020, titled “Relationship andCredibility Based Experience Rating and Skill Discovery System”, filedin United States Patent and Trademark Office on Jul. 9, 2019, whichclaims priority to and the benefit of the provisional patent applicationNo. 62/696,327, titled “Relationship and Credibility Based ExperienceRating and Skill Discovery System”, filed in United States Patent andTrademark Office on Jul. 10, 2018. The specifications of the abovereferenced patent applications are incorporated herein by reference intheir entirety.

BACKGROUND

Businesses that provide services to match opportunity seekers orcandidates to opportunities, for example, job openings, need to build anobjective method to assess the skill level of candidates. Most existingmethods that require a candidate to indicate his/her skills are subjectto error and/or deliberate misrepresentation. Often, experiencesummaries of the candidates for the opportunities overstate theirqualifications and are not a reliable indicia of the experience, skills,and accomplishments of the candidate. Submitting resumes is the mostwidely accepted first step for an opportunity, for example, a jobopening, and thus, the resumes act as a source for the experiencesummaries. Online career platforms also allow individuals to manuallyinput their experience summaries and skills. Experience summaries oftenprovide an inaccurate view of the experience of the opportunity seekers.However, the credibility of the candidate's qualifications cannot beassessed from experience summaries alone, and related documentation suchas a full resume of the opportunity seeker most often fails to establishcredibility of the candidate's qualifications. An accurate appraisal ofthe credibility of a job candidate's experience summary would allow, forexample, a hiring manager, to make better judgments during the hiringprocess, for example, to determine which job candidates to bring in fora job interview. Therefore, there is a need for accessing thecredibility of the experience summaries provided by a job candidateprior to the hiring process.

One reason to consider experience summaries as a qualification criterionfor an opportunity is to determine if certain desired attributes, forexample, skills or traits required for the job are possessed by thecandidate as recited in the candidate's experience summary. Consider anexample of a prospective employer that needs candidates with skills inhypertext preprocessor (PHP) programming language. The prospectiveemployer examines the experience summary of the candidate to determinewhether the PHP programming language was used by the candidate at his orher job. If there was a clear association between the experiencesummaries and the skills used by the candidate at his employment, thecomputer implemented searches performed by online career platforms willidentify candidates with the experience and skills required. Therefore,there is a need for identifying skills and traits of the candidatesassociated with the claims of the candidate as recited in thecandidate's experience summary.

Experience summaries often comprise skills and traits of the opportunityseekers that is difficult for computerized systems, for example, onlinecareer platforms to identify. The skills in the experience summary maynot use any known keywords associated with a skill, and although manyexisting technologies, for example, parsing and natural languageprocessing, yield a data structure, these technologies still do notcontain associations of the skills with the claims of the experiencesummaries that a person would likely determine. Consider an examplewhere a skill in the experience summary of a developer at Company X isassociated to the skills in the hypertext preprocessor (PHP) programminglanguage, the Apache web server, and website development, and to a traitsuch as being “results oriented”. However, existing technologies are notcapable of determining the association of the claimed skill in theexperience summary to the skills and traits of the opportunity seekers.Therefore, there is a need for discovering skills and traits associatedwith the claims of the experience summaries without reliance on computeranalysis of text of the claims of the experience summaries.

One way to verify claimed skills in the experience summaries and skillsof the opportunity seekers is to obtain opinions of other people, thatis, reviewers who have a reasonable basis to know about the jobexperience, the skills used, and the traits claimed in the experiencesummary of the opportunity seekers. Therefore, there is a need foridentifying reviewers who have a reasonable basis to know about the jobexperience, the skills used, and the traits present in a job experiencedescribed by an experience summary. Furthermore, there is a need forcollecting opinions from these identified reviewers about the jobexperience, the skills used, and the traits present in the jobexperience in the experience summary submitted by a job seeker. A sourceof information is reviewers who have a reasonable basis to know aboutthe job experience, the skills used, and the traits present in a jobexperience described in an experience summary of an opportunity seekerwho submitted the experience summary. Therefore, there is a need foridentifying one or more reviewers who have a reasonable basis to knowabout the job experience, the skills used, and the traits present in ajob experience described in an experience summary provided by theopportunity seeker.

An opportunity seeker asserting that a reviewer has a reasonable basisto know about a claimed skill in the experience summaries does not meanthat the reasonable basis actually exists. One premise for a reasonablebasis is if the two users, that is, the opportunity seeker and thereviewer had a working relationship with each other relating to the jobexperience described in the experience summary. Working relationshipscan vary in depth of knowledge of the experience summary, for example,seeing each other in the kitchen as compared to co-developing a product.The deeper the knowledge of an experience summary, the more credible isthe opinion related to the experience summary. Therefore, there is aneed for discovering the working relationship between the opportunityseeker providing the experience summary and the reviewers providingopinions about the job seeker's experience summary. Moreover, there is aneed for discovering the depth of knowledge of the working relationshipbetween the opportunity seeker and the reviewers. Furthermore, there isa need for using the depth of knowledge of the job experience as afactor when calculating the credibility of the opinions about theexperience summary, the skills used, and the traits present in the jobexperience.

Opinions provided by the reviewers about the experience summaries, theskills used on the job, and the traits present in a specific jobexperience submitted by a job seeker in an experience summary aresubjective and may be influenced by biases and errors, for example,where a personal friendship exists between the reviewer and theopportunity seeker, the reviewer may give a high score to the claimedskill in the experience summary of the opportunity seeker. Therefore,there is a need for evaluating the credibility of opinions provided bythe reviewers regarding the experience summary, the skills used, and thetraits present in the job experience provided in the experience summary.Using reviews of the experience summaries from more than one reviewerreviewers is a way to reduce the effect of bias from the reviewerssubmitting their opinions. The opinions from one or more reviewers maydiffer in ratings assigned and in the credibility of the ratings.Therefore, there is a need for collecting the opinions from multiplereviewers about the experience summary, the skills used, and the traitspresent in a job experience described in an experience summary. However,for the opinions of one or more reviewers to be most effectively usedfor selecting a suitable opportunity seeker, a single rating is neededfor each skill and experience summary of the opportunity seeker, evenwhen there are multiple ratings from different reviewers. However, notall ratings are equally credible. Therefore, there is a need foraggregating the ratings of an experience summary into a single score andfor aggregating the ratings about each of the skills and the traits intoa single rating per skill and trait, along with considering thecredibility of the ratings provided by the reviewers as a weighingfactor.

Hence, there is a long felt but unresolved need for a system and amethod for determining credibility of experience ratings provided by oneor more reviewers and discovering skills of opportunity seekers based ona relationship between the reviewers and the opportunity seekers.

SUMMARY OF THE INVENTION

This summary is provided to introduce a selection of concepts in asimplified form that are further disclosed in the detailed descriptionof the invention. This summary is not intended to determine the scope ofthe claimed subject matter.

The experience rating and skill discovery system (ERSDS) and the methoddisclosed herein address the above recited needs for determining thecredibility of experience ratings provided by one or more reviewers, andfor discovering skills of opportunity seekers based on a relationshipbetween the reviewers and the opportunity seekers. A user, for example,an opportunity seeker via seeker devices, selects an experience summarydata set in an experience summary list to be rated and associates theexperience summary data set with a skills profile list, creating a skillprofile first if needed. The opportunity seeker further invites anynumber of reviewers to evaluate the experience summary data set foroverall plausibility and rate the skills in the skills profile list ofthe opportunity seeker. The reviewers who choose to rate the experiencesummary data sets and the skills of the opportunity seekers use agraphical user interface, for example, web screens to enter the ratingscorresponding to the experience summary data sets and the skills of theopportunity seeker. A relationship measurement module external to theERSDS computes a relationship depth of the relationship between theopportunity seeker and the reviewer. A credibility module of anoperational system external to the ERSDS computes rating credibilitymeasures indicating credibility of the ratings from the reviewers. Atvarious times, possibly after every review by the reviewers, possibly asa batch runs, or possibly on demand, an aggregation process is run onthe ratings of the experience summary data sets and the skills of theopportunity seeker by the ERSDS and an aggregated experienceplausibility measure and an aggregated experience credibility measureare assigned as a score corresponding to the experience summary dataset, and an aggregated skill amount measure and an aggregated skillcredibility measure are assigned as a score corresponding to each of theskills of the opportunity seeker associated with a skill profile.

The aggregated experience plausibility measure and the aggregatedexperience credibility measure establish accuracy of the experiencesummary data sets. The skills associated with the skill profile thathave a large skill amount measure of sufficient credibility act as anidentification of the domains of expertise and personal traitsassociated with the experience summary. No computer analysis of the textof claims of experience summaries is needed to discover the skillsassociated with the claims of the experience summaries. The experiencerating and skill discovery system (ERSDS) enables an opportunity seeker,also referred to as a “rated user”, to supply information on thereviewers who knew of an experience, for example, a job experience ofthe opportunity seeker via invitations. The invitations fulfill the needto discover reviewers who have a reasonable basis to know about theexperience and the skills used in an experience described by anexperience summary. Furthermore, the ERSDS collects opinions from theinvited reviewers about the experience, the skills used, and thepersonal traits present in an experience described by an experiencesummary. By means of the invitations, the ERSDS collects from the rateduser providing the experience summary, the identities of the reviewerswho have a reasonable basis to know about the experience and the skillsused in an experience described by an experience summary.

The experience rating and skill discovery system (ERSDS) collectsratings from the discovered reviewers, thereby fulfilling the need tocollect opinions about the experience summaries and the skills presentin a specific experience described by an experience summary.Furthermore, multiple reviewers submit ratings about the skills and theexperience summary, thereby fulfilling the need to collect the opinionsfrom multiple parties about the experience summary and the skillspresent in an experience described by an experience summary. A userassociation module of the ERSDS collects relationship data comprising,for example, information about a working relationship between theopportunity seeker and the reviewer, thereby fulfilling the need todiscover the working relationship between the opportunity seekerproviding the experience summary and those reviewers providing opinionsabout the experience summary. The relationship measurement moduleexternal to the ERSDS and invoked by the ERSDS fulfills the need todiscover the depth of knowledge of the working relationship, that is, arelationship depth of the relationships between the opportunity seekerand the reviewers. The credibility module external to the ERSDS andinvoked by the ERSDS runs reviews through the ratings provided by thereviewers, thereby fulfilling the need to evaluate the credibility ofopinions about the experience summary and the skills present in anexperience described by the experience summary. The credibility modulehas access to the relationship depth, thereby fulfilling the need to usethe depth of knowledge of the experience as a factor when calculatingthe credibility of the opinions about the experience summary and theskills used in the experience. An aggregation module of the ERSDSproduces single ratings from multiple ratings, that is, aggregatesratings of the experience summary data sets and the skills and generatesan aggregated experience plausibility measure and an aggregatedexperience credibility measure, and for each skill or personal trait ina skill profile, generates an aggregated skill amount measure and anaggregated skill credibility measure.

The aggregation module uses credibility of the ratings to affect theimpact of each individual rating for each skill in the skill profile,thereby fulfilling the need to aggregate the ratings of an experiencesummary into a single score and to aggregate the ratings about eachskill into a single rating per skill using credibility of the opinionsas a factor in the process of aggregation. The skills with correspondingaggregated skill amount measures and aggregated skill credibilitymeasures are used by another operational system of an entity fordetermining a degree of match between the skills with the correspondingaggregated skill amount measures and the aggregated skill credibilitymeasures and opportunity description skills of varying importance inopportunity descriptions. The experience summary data set with acorresponding aggregated experience plausibility measure and anaggregated experience credibility measure are used by anotheroperational system of the entity for recommending which experiencesummary data sets are worth consideration when reviewing an opportunityseeker. The relationship measurement module and the credibility moduleexamine all records related to either the opportunity seeker or theinvited reviewer. The rating module in communication with therelationship measurement module, for example, poses questions about therelationship of the invited reviewers with other invited reviewers. Thecredibility module notices that the invited reviewer gives implausiblyhigh scores in reviews of other opportunity seekers and can use that asa factor in determining credibility of the ratings provided by thereviewers. The credibility module also takes into account scoresprovided from a first time reviewer versus a repeated reviewer fordetermining the credibility of the ratings provided by the reviewers.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofthe invention, is better understood when read in conjunction with theappended drawings. For illustrating the invention, exemplaryconstructions of the invention are shown in the drawings. However, theinvention is not limited to the specific methods and componentsdisclosed herein.

FIG. 1 exemplarily illustrates an experience rating and skill discoverysystem (ERSDS) incorporating a computer system architecture for using arelationship between one or more reviewers and opportunity seekers as afactor in determining credibility of experience ratings provided by oneor more reviewers and discovering skills of opportunity seekers from theexperience ratings provided by the reviewers.

FIG. 2 exemplarily illustrates a flow diagram comprising the stepsperformed by an experience rating and skill discovery system fordetermining credibility of experience ratings provided by one or morereviewers and discovering skills of opportunity seekers based on arelationship between the reviewers and the opportunity seekers.

FIG. 3 exemplarily illustrates a schematic diagram showing a skillprofile module of the experience rating and skill discovery system thatuses profile data in a user profile list and a predefined skill list togenerate a skills profile list.

FIG. 4 exemplarily illustrates a flow diagram comprising the steps forinviting a reviewer to review experience summary data sets and skills ofan opportunity seeker.

FIG. 5 exemplarily illustrates a flow diagram comprising the stepsperformed by a user association module of the experience rating andskill discovery system for creating a relationship record in arelationship list.

FIG. 6 exemplarily illustrates a flowchart comprising the stepsperformed by the user association module for finding or adding areviewer to the user profile list.

FIG. 7 exemplarily illustrates a flowchart comprising the steps forselecting an experience summary data set with a corresponding skillprofile name from an experience summary list by an opportunity seeker.

FIG. 8 exemplarily illustrates a schematic diagram showing the userassociation module that collects relationship data from an opportunityseeker about a relationship record created in the relationship list.

FIG. 9 exemplarily illustrates a flow diagram comprising the stepsperformed by a hash module for generating a uniform resource locatorlink for rendering graphical user interfaces on a reviewer device.

FIG. 10 exemplarily illustrates a flow diagram comprising the stepsperformed by the rating module for receiving ratings corresponding toeach of the skills associated with the experience summary data elementsand a reviewer plausibility measure corresponding to each of theexperience summary data elements from the reviewers and the stepsperformed by a relationship measurement module and a credibility moduleexternal to the experience rating and skill discovery system for scoringthe received ratings and the relationship data.

FIG. 11 exemplarily illustrates a flow diagram comprising the steps forproviding ratings corresponding to each of the skills associated withthe experience summary data sets of an opportunity seeker, by areviewer.

FIG. 12 exemplarily illustrates a schematic diagram showing the ratingmodule that invokes a relationship measurement module to compute andsent the relationship depth of the relationship record in therelationship list.

FIG. 13 exemplarily illustrates a flow diagram comprising the stepsperformed by the rating module for receiving and configuring a reviewerplausibility measure of an experience summary data set from a reviewerdevice.

FIG. 14 exemplarily illustrates a flow diagram comprising the stepsperformed by the rating module for receiving ratings corresponding toskills in the skills profile list from a reviewer device.

FIG. 15 exemplarily illustrates a schematic diagram showing therelationship measurement module that computes a relationship depth of arelationship between an opportunity seeker and a reviewer.

FIG. 16 exemplarily illustrates a schematic diagram showing thecredibility module that computes and stores rating credibility measuresof the skills.

FIG. 17 exemplarily illustrates flow diagrams comprising the stepsperformed by an aggregation module of the experience rating and skilldiscovery system for aggregating relationship data and ratings.

FIG. 18 exemplarily illustrates a flow diagram comprising the stepsperformed by a measure aggregator invoked by the aggregation module forcomputing an aggregated experience plausibility measure and anaggregated experience credibility measure for a single experiencesummary data set.

FIG. 19 exemplarily illustrates a flow diagram comprising the stepsperformed by the measure aggregator for computing an aggregatedexperience plausibility measure or an aggregated skill amount measureand an aggregated experience credibility measure or an aggregated skillcredibility measure.

FIG. 20 exemplarily illustrates a flow diagram comprising the stepsperformed by the measure aggregator for generating the aggregatedexperience credibility measure and the aggregated experienceplausibility measure.

FIG. 21 exemplarily illustrates a flow diagram comprising the stepsperformed by the aggregation module for computing an aggregated skillamount measure and an aggregated skill credibility measure for a singleskill in the skills profile list.

FIGS. 22A-22C exemplarily illustrates a method for determiningcredibility of experience ratings provided by one or more reviewers anddiscovering skills of opportunity seekers based on a relationshipbetween the reviewers and the opportunity seekers.

FIG. 23 exemplarily illustrates a predefined skill list comprisingskills classified into personal traits and domains of expertise.

FIG. 24 exemplarily illustrates a user profile list comprising profiledata of users of the experience rating and skill discovery system.

FIG. 25 exemplarily illustrates a skills profile list generated by theskill profile module using the profile data in the user profile listexemplarily illustrated in FIG. 24 and the skills in the predefinedskill list exemplarily illustrated in FIG. 23 .

FIG. 26 exemplarily illustrates an experience summary list received bythe user association module.

FIG. 27 exemplarily illustrates a relationship list comprisingrelationship data, a reviewer plausibility measure, and a reviewercredibility measure of each of the experience summary data sets listedin the experience summary list exemplarily illustrated in FIG. 26 .

FIG. 28 exemplarily illustrates a ratings list comprising ratings andrelationship depths corresponding to the skills, generated by therelationship measurement module.

FIG. 29 exemplarily illustrates a user profile list comprising profiledata of at least one user, stored in a database of the experience ratingand skill discovery system.

FIG. 30 exemplarily illustrates an experience summary list comprising atleast one experience summary data set, stored in a database of theexperience rating and skill discovery system.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 exemplarily illustrates an experience rating and skill discoverysystem (ERSDS) 100 incorporating a computer system architecture forusing a relationship between one or more reviewers and opportunityseekers as a factor in determining credibility of experience ratingsprovided by one or more reviewers and discovering skills of opportunityseekers from the experience ratings provided by the reviewers. As usedherein, “skill” refers to an expertise of an opportunity seeker in aparticular domain for carrying out an opportunity smoothly andefficiently. The skills of the opportunity seeker comprise, for example,personal traits and/or domains of expertise of the opportunity seeker.As used herein, “personal traits” refer to distinguishing qualities ofthe opportunity seekers. The personal traits comprise, for example,dependability, integrity, confidence, etc. A reviewer is an individualwho reviews and provides experience ratings to an experience summaryprovided by the opportunity seeker for an opportunity in an entity, forexample, a company or an organization. The experience summary of theopportunity seeker is a summary or a gist of relevant past experiences,for example, job experiences, of the opportunity seeker in differentopportunities, for example, job roles. Also, as used herein, “experienceratings” refer to quantized values assigned by reviewers to experiencesummaries provided by opportunity seekers on assessment of theexperience summaries. The relationship between a reviewer and anopportunity seeker is, for example, a relationship between a manager anda subordinate, a relationship between co-workers, etc. The details ofthe relationship result in a relationship depth score that is a factorin the credibility given to the experience ratings provided by thereviewers. “Experience ratings” is not limited to a hiring industry, butis applicable to numerous industries ranging, for example, professionalservices for example services provided by doctors, lawyers, etc.,restaurants, any service provided by a service provider, a product, etc.

The experience rating and skill discovery system (ERSDS) 100 isimplemented as a software as a service, for example, a hiring platformas a service (HPaaS). In an embodiment, the ERSDS 100 is implemented,for example, as a rating platform as a service (RPaaS). In anembodiment, the ERSDS 100 is configured as a web based platform, forexample, a website hosted on a server or a network of servers. A list ofskills possessed by opportunity seekers and experience ratings allowdiscovery of the skills associated with an experience summary. Based onthe skills entered by the opportunity seeker and the experience ratingsprovided by the reviewers, the ERSDS 100 discovers skills of theopportunity seekers from the experience summary provided by theopportunity seekers. The skills provided by the opportunity seekers as apart of a skills profile list 303 exemplarily illustrated in FIG. 25 ,comprise personal traits and domains of expertise of the opportunityseekers. The personal traits are flagged using an “ISTRAIT” flag in apredefined skill list 302 exemplarily illustrated in FIG. 23 .

The experience rating and skill discovery system (ERSDS) 100 comprisesat least one web computer server 101, at least one database server 104,and at least one processing computer server 106. The web computer server101 renders graphical user interfaces 101 a to multiple seeker devices102 and multiple reviewer devices 103. As used herein, “seeker devices”and “reviewer devices” are user devices, for example, personalcomputers, laptops, tablet computing devices, smart phones, mobilecomputers, personal digital assistants, touch centric devices,workstations, client devices, portable electronic devices, networkenabled computing devices, interactive network enabled communicationdevices, etc., possessed by the opportunity seekers and the reviewersrespectively, for interacting with the ERSDS 100. In an embodiment, theseeker devices 102 and reviewer devices 103 are hybrid computing devicesthat combine the functionality of multiple devices. Examples of a hybridcomputing device comprise a cellular telephone that includes a mediaplayer functionality, a gaming device that includes a wirelesscommunications capability, a cellular telephone that includes a documentreader and multimedia functions, and a portable device that has networkbrowsing, document rendering, and network communication capabilities.For purposes of illustration, the seeker devices 102 and reviewerdevices 103 are user devices of a recruitment system of entities such asoffices, educational institutes, etc. The database server 104 iscommunicatively coupled to the web computer server 101 via a network105, for example, one of the internet, an intranet, a wired network, awireless network, a communication network that implements Bluetooth® ofBluetooth Sig, Inc., a network that implements Wi-Fi® of Wi-Fi AllianceCorporation, an ultra-wideband communication network (UWB), a wirelessuniversal serial bus (USB) communication network, a communicationnetwork that implements ZigBee® of ZigBee Alliance Corporation, ageneral packet radio service (GPRS) network, a mobile telecommunicationnetwork such as a global system for mobile (GSM) communications network,a code division multiple access (CDMA) network, a third generation (3G)mobile communication network, a fourth generation (4G) mobilecommunication network, a fifth generation (5G) mobile communicationnetwork, a long-term evolution (LTE) mobile communication network, apublic telephone network, etc., a local area network, a wide areanetwork, an internet connection network, an infrared communicationnetwork, etc., or a network formed from any combination of thesenetworks. In an embodiment, the experience rating and skill discoverysystem 100 is accessible to the satellite internet of users, forexample, through a broad spectrum of technologies and devices such ascellular phones, tablet computing devices, etc., with access to theinternet.

The database server 104 hosts one or more databases 104 a for storing auser profile list 301 exemplarily illustrated in FIG. 24 , the skillsprofile list 303 exemplarily illustrated in FIG. 25 , a predefined skilllist 302 exemplarily illustrated in FIG. 23 , an experience summary list802 exemplarily illustrated in FIG. 26 , a relationship list 801exemplarily illustrated in FIG. 27 , and a ratings list 803 exemplarilyillustrated in FIG. 28 . The processing computer server 106 comprises atleast one processor 107 communicatively coupled to the web computerserver 101, the database server 104, the seeker devices 102, and thereviewer devices 103 via the network 105. The processor 107 executescomputer program instructions defined by modules of the experiencerating and skill discovery system (ERSDS) 100. The modules of the ERSDS100 comprise a skill profile module 108 a, a user association module 108b, an invitation module 108 c, a rating module 108 d, and an aggregationmodule 108 e. The modules external to the ERSDS 100 and invoked by theERSDS 100 comprise a relationship measurement module 239 exemplarilyillustrated in FIG. 15 , a credibility module 240 exemplarilyillustrated in FIG. 16 , and a hash module (not shown). The processingcomputer server 106 further comprises a non-transitory computer readablestorage medium having embodied thereon, computer program codescomprising instructions executable by the at least one processor 107 fordetermining the credibility of experience ratings provided by the one ormore reviewers and discovering skills of the opportunity seekers basedon the relationship between the reviewers and the opportunity seekers.

The skill profile module 108 a of the experience rating and skilldiscovery system (ERSDS) 100 reads profile data of the opportunityseekers that is stored in the user profile list 301 exemplarilyillustrated in FIG. 24 . As used herein, “profile data” refers toidentification information of the opportunity seekers and the reviewerswho use the ERSDS 100. The profile data comprises, for example, uniqueuser identifiers (IDs), first names, last names, and electronic mail(email) address. The skill profile module 108 a generates the skillsprofile list 303 exemplarily illustrated in FIG. 25 , comprising skillprofiles associated with the opportunity seekers, using the storedprofile data of the opportunity seekers and the skills selected from thepredefined skill list 302 exemplarily illustrated in FIG. 23 , via theseeker devices 102. The skill profiles comprise skills of theopportunity seeker with corresponding skill amount measures andcorresponding skill credibility measures indicating credibility of theskill amount measures. As used herein, “skill profile” refers to anentry or a subset of the skills profile list 303 identified by a useridentifier, that is, USER_ID of the opportunity seeker and a skillprofile name. The USER_ID and the skill profile name form a unique keyin the skills profile list 303. The skills in the skills profile list303 associated with the opportunity seekers are identified by a skillprofile name and have corresponding skill amount measures andcorresponding skill credibility measures indicating credibility of theskill amount measures. In a skill profile, each of the skills associatedwith an opportunity seeker occurs at most once with a correspondingskill amount measure and a corresponding skill credibility measure. Theskill amount measure will be NULL and the skill credibility measure willbe NULL for each of the selected skills when a skill profile in theskills profile list 303 is generated by the skill profile module 108 a.The values for the skill amount measure and the skill credibilitymeasure for a skill are obtained and filled in the skill profile as anaggregation of the skill amount measures and the skill credibilitymeasures entered by reviewers on evaluating the skills associated withthe opportunity seekers.

As used herein, a “skill amount measure” is a quantized value ofproficiency of an opportunity seeker in a skill. The skill amountmeasure is a numerical value between 0 and 1, both inclusive and NULL.The skill amount measure represents the degree to which a skill ispresent. A value of 1 for the skill amount measure indicates that theskill is present to a maximum level possible, that is, the opportunityseeker is highly proficient in the skill. A value of 0 for the skillamount measure indicates that the skill is not present, that is, theopportunity seeker does not possess the skill. A value of NULL for theskill amount measure indicates that the skill is not known. The skillamount measure is a fraction of a total skill amount measure of theskills possessed by the opportunity seekers. Also, as used herein,“skill credibility measure” refers to a numerical value between 0 and 1,both inclusive and NULL. The skill credibility measure represents theprobability of the skill amount measure being accurate. The predefinedskill list 302 exemplarily illustrated in FIG. 23 , comprisesopportunity seekers skills classified into personal traits and domainsof expertise. The skills in the predefined skill list 302 arerepresented as non-null strings and flagged using an “ISTRAIT” flag. TheISTRAIT flag is a non-NULL Boolean. The skills of the opportunity seekerin the skill profile are selected from the predefined skill list 302 bythe opportunity seekers via the seeker devices 102.

The user association module 108 b of the experience rating and skilldiscovery system (ERSDS) 100 receives the experience summary list 802exemplarily illustrated in FIG. 26 , from the database server 104. Theexperience summary list 802 comprises experience summary data elementsin the skill profiles listed in the skills profile list 303 exemplarilyillustrated in FIG. 25 , of the opportunity seekers identified by aUSER_ID, with corresponding experience plausibility measures andcorresponding experience credibility measures. An experience summarydata element, a USER_ID in the skill profile, a start date of theexperience summary data element START_DATE, an end date of theexperience summary data element END_DATE, an experience plausibilitymeasure, and an experience credibility measure corresponding to theexperience summary data element constitute an “experience summary dataset”. The experience summary data sets are entries in the experiencesummary list 802. An experience summary data set is associated with askill profile. In an embodiment, the same skill profile is associatedwith multiple experience summary data sets, if an opportunity seekerchanges entities while doing the same work or job. The user associationmodule 108 b selects an experience summary data set from the experiencesummary list 802 and creates a record in the relationship list 801exemplarily illustrated in FIG. 27 , with a corresponding experienceidentifier, for example, EXP_ID from the experience summary data set.The experience summary data set is identified by the EXP_ID in theexperience summary list 802. If a skill profile name in the experiencesummary data set is missing, the user association module 108 b assigns askill profile name as disclosed in the detailed description of FIG. 7 .The selected experience summary data set comprises a skill profile name.The experience summary data element in an experience summary data setrepresents responsibilities performed by opportunity seekers in a skillprofile over a duration of time. A responsibility performed by anopportunity seeker is represented by a string describing, for example, ajob related experience in a past entity such as a company, listing coretasks performed as a part of the job related experience.

Also, as used herein, “experience plausibility measure” refers to anumerical value representing the amount of accuracy of the experiencesummary data set. The experience plausibility measure is a numericalvalue between 0 and 1, both inclusive and NULL. A value of 1 for theexperience plausibility measure represents an experience summary dataelement and the associated dates to be totally accurate. A value of 0for the experience plausibility measure represents an experience summarydata element and the associated dates to be entirely fictitious. Theexperience credibility measure represents the probability of theexperience plausibility measure being accurate. The experiencecredibility measure is a numerical value between 0 and 1, both inclusiveand NULL.

The invitation module 108 c of the experience rating and skill discoverysystem (ERSDS) 100, invoked by the user association module 108 b,transmits invitations comprising a uniform resource locator (URL) link,to the reviewer devices 103 for evaluating the experience summary dataelements in the received experience summary list 802 exemplarilyillustrated in FIG. 26 , and the skills of the opportunity seeker in thegenerated skills profile list 303 exemplarily illustrated in FIG. 25 .The evaluation of the experience summary data elements and the skillsallows the reviewers to discover the skills possessed by an opportunityseeker in a skill profile associated with the experience summary dataelements in the experience summary list 802. The hash module external tothe ERSDS 100, in communication with the user association module 108 band the invitation module 108 c, generates authentication credentialsand the URL link comprising a hash for the graphical user interface 101a to be rendered on the reviewer devices 103 of the reviewers. Using theinvitations, the reviewers evaluate the experience summary data sets andaward ratings to each of the skills associated with the experiencesummary data elements in a skill profile.

The user association module 108 b determines whether profile data of thereviewers with the transmitted invitations is available in the userprofile list 301. If the profile data is unavailable, the userassociation module 108 b receives and stores unavailable profile data ofthe reviewers in the user profile list 301. In an embodiment, the userassociation module 108 b receives profile data of a reviewer from anopportunity seeker, determines whether the profile data of the revieweris available in the user profile list 301, and stores unavailableprofile data of the reviewer in the user profile list 301. Theopportunity seekers invoke the user association module 108 b byproviding the profile data of a reviewer from whom the opportunityseeker wants a review. The invitation module, in communication with theuser association module 108 b, transmits an invitation to the reviewerdevice of the reviewer. The reviewer, also referred herein as the“rating user”, is a human identified by a USER_ID in the user profilelist 301 exemplarily illustrated in FIG. 24 . In the relationship list801 exemplarily illustrated in FIG. 27 , the USER_ID of an opportunityseeker, also referred herein as the “rated user”, is stored, forexample, as RATED_USER_ID, and the USER_ID of the rating user is stored,for example, as RATING_USER_ID indicating one user of the experiencerating and skill discovery system (ERSDS) 100 can rate another user. Inan embodiment, an entry in the relationship list 801 need notnecessarily indicate that an invitation is transmitted to the ratinguser from the rated user by the invitation module. In this embodiment,the rating user finds out by other means, for example, by already beinga user of the ERSDS, from the graphical user interfaces 101 a of thereviewer devices 103, etc. For every skill in the skill profileassociated with the rated user with a RATED_USER_ID in the relationshiplist 801, there exists a corresponding row in the ratings list 803exemplarily illustrated in FIG. 28 , with a relationship identifier, forexample, REL_ID, from the relationship list 801 and the skill. In anembodiment, the user association module 108 b prompts the reviewer toreview an opportunity seeker that the reviewer recognizes by questions,for example, “Hey, you are reviewing Joe who worked at Amazon. Did youalso know Xudong?” from a list of “other coworkers you might know” or“Who else knew Joe?”. Such prompting by the user association module 108b, in an embodiment, causes invitations to be sent to the reviewer.

The user association module 108 b stores an association between each ofthe opportunity seekers and each of the reviewers as relationship datain the relationship list 801. The information of the relationshipsbetween the reviewers and the opportunity seekers is indicated using theUSER_ID of the opportunity seeker, that is, RATED_USER_ID, the USER_IDof the reviewer, that is, RATING_USER_ID, and the EXP_ID of theexperience summary data set being evaluated by the reviewer. The userassociation module 108 b configures a reviewer plausibility measurecorresponding to each experience summary data set in the relationshiplist 801 to NULL. As used herein, “reviewer plausibility measure” refersto a numerical value representing the amount of accuracy of theexperience summary data element as estimated by a reviewer. The reviewerplausibility measure is a numerical value between 0 and 1, bothinclusive and NULL. A value of 1 of the reviewer plausibility measurerepresents an experience summary data set to be totally accurate. Avalue of 0 of the reviewer plausibility measure represents an experiencesummary data set to be fictitious. Each record in the relationship list801 corresponding to a relationship between a reviewer and anopportunity seeker associated with an experience summary data setidentified by the EXP_ID is identified using a unique relationshipidentifier REL_ID. The relationship list 801 comprises a reviewercredibility measure corresponding to each experience summary data set.The reviewer credibility measure represents the probability of thereviewer plausibility measure being accurate. The reviewer credibilitymeasure is also a numerical value between 0 and 1, both inclusive andNULL. The reviewer credibility measure indicates credibility of thereviewer plausibility measure corresponding to each of the experiencesummary data elements in the skill profiles of the opportunity seekers.The experience summary data set occurs only once in the experiencesummary list 802 exemplarily illustrated in FIG. 26 , as the EXP_IDidentifies the experience summary data set. In the relationship list801, the experience summary data set occurs multiple times.

The user association module 108 b of the experience rating and skilldiscovery system (ERSDS) 100 invokes the relationship measurement module239 of an operational system of an entity external to the ERSDS 100, forrendering questions related to the relationship between each of theopportunity seekers and each of the reviewers and collects therelationship data. The user association module 108 b invokes therelationship measurement module 239 to ask questions and receiveresponses, for example, “What was your working relationship: He was mymanager”, to and from the opportunity seeker respectively, when therelationship is created. The user association module 108 b does notinteract with a reviewer and cannot ask questions to the reviewer. Theuser association module 108 b stores the collected relationship datacomprising information of the relationship in the relationship list 801exemplarily illustrated in FIG. 27 .

The rating module 108 d receives the reviewer plausibility measurecorresponding to each of the experience summary data elements in theskill profiles of the opportunity seekers from the reviewer devices andupdates the received reviewer plausibility measure in the relationshiplist 801. The rating module 108 d receives ratings provided by each ofthe reviewers from the reviewer devices on evaluating the skillsassociated with the experience summary data elements in the skillprofiles. A reviewer in a relationship identified by the REL_ID in therelationship list 801 exemplarily illustrated in FIG. 27 , with anopportunity seeker, evaluates the skills associated with a skill profilename from the experience summary data set identified by the EXP_ID. Thereviewer awards ratings for each of the skills possessed by theopportunity seeker. The ratings comprise a rating skill amount measureand a strength of belief measure for each of the skills. As used herein,a “rating skill amount measure” refers to a quantized value ofproficiency of the opportunity seekers in the skills as assessed by thereviewers. The rating skill amount measure is a non-negative numericalvalue between 0 and 1 inclusive or NULL. The rating skill amount measurerepresents the degree to which a skill is present in the opportunityseeker. Also, as used herein, “strength of belief measure” refers to aquantized value indicating probability of the opportunity seekerpossessing the skill. The strength of belief measure is also anon-negative numerical value or NULL. The strength of belief is aself-assessment from the reviewer and indicates the difference between,for example, “I know that Joe is an expert in the hypertext preprocessor(PHP) programming language” and “I sort of think that Joe is an expertin the PHP programming language”. The reviewers themselves may have nocredibility and what they say may have credibility.

The rating module 108 d updates the ratings list 803 exemplarilyillustrated in FIG. 28 , comprising the received ratings andrelationship depths corresponding to each of the skills. As exemplarilyillustrated in FIG. 28 , a reviewer in a relationship identified by theREL_ID with an opportunity seeker associated with an experience summarydata set identified by the EXP_ID awards a rating skill amount measureAMOUNT_PRESENT and a strength of belief measure STRENGTH OF BELIEF foreach of the skills in performing the experience summary data element inthe experience summary data set identified by the EXP_ID. The userassociation module 108 b generates the ratings list 803 and configuresthe rating skill amount measure and the strength of belief measure foreach of the skills in the ratings list 803 to NULL.

The relationship measurement module 239, invoked by the user associationmodule 108 b and the rating module 108 d, computes the relationshipdepth of the relationship between each of the reviewers and each of theopportunity seekers using the relationship data associated with each ofthe skills stored in the relationship list 801 exemplarily illustratedin FIG. 27 . As used herein, “relationship depth” refers to a quantizedvalue indicating depth of knowledge possessed by a reviewer about theexperience summary data element associated with an opportunity seeker.That is, the relationship depth gauges the quality of interactionsbetween the reviewer and the opportunity seeker in the course of theopportunity seeker having performed the described activities in theexperience summary data set. The relationship depth is a non-negativenumerical value or NULL. The rating module 108 d interacts with thereviewer and asks the reviewer about the relationship with theopportunity seeker before proceeding to receive ratings, for example, as“What was your working relationship: He was a co-worker, I did notmanage him”. The relationship depth is updated for the relationship inthe ratings list 803 exemplarily illustrated in FIG. 28 , and therelationship list 801. The relationship measurement module 239 gathersand stores the additional relationship details about the relationshipbetween the reviewer and the opportunity seeker. A generic computerusing a generic program cannot compute relationship depth of therelationship between each of the reviewers and each of the opportunityseekers using the relationship data associated with each of the skillsas disclosed above.

The rating module 108 d of the experience rating and skill discoverysystem (ERSDS) 100, in communication with the credibility module 240 ofthe operational system of the entity external to the ERSDS 100, receivesthe reviewer credibility measure of each of the experience summary dataelements in the skill profiles of the opportunity seekers from thecredibility module 240. The rating module 108 d stores the receivedreviewer credibility measure in the relationship list 801 exemplarilyillustrated in FIG. 27 . The credibility module 240 computes thereviewer credibility measure of each of the experience summary dataelements. The rating module 108 d receives a rating credibility measureof each of the skills from the credibility module 240 and stores eachrating credibility measure in the ratings list 803 exemplarilyillustrated in FIG. 28 . As used herein, “rating credibility measure”refers to a numerical value indicating estimated credibility of therating skill amount measure corresponding to the skills possessed by theopportunity seekers provided by the reviewers via the reviewer devicesto the credibility module 240. The rating credibility measure indicatescredibility of the rating skill amount measure of each of the skills.The reviewer credibility measure and the rating credibility measure arenot entered by humans and are not received from humans. Prior toreceiving ratings, the rating module 108 d poses additional questions tothe reviewer and the rating module 108 d, in communication with therelationship measurement module 239, updates the relationship depth inthe ratings list 803 and the relationship list 801. In an embodiment,the rating module 108 d does not collect ratings for a particular skillfrom the reviewers when the relationship depth is lower than athreshold. In another embodiment, the rating module 108 d downgradessome skills possessed by the opportunity seekers because the skills didnot receive ratings from the reviewers. The credibility module 240invoked by the rating module 108 d computes the rating credibilitymeasure. The rating credibility measure is a non-negative numericalvalue or NULL. Later on, possibly many days later after a review, thatis, after the ratings and the reviewer plausibility measure are entered,the credibility module 240 is invoked, and the credibility module 240updates the rating credibility measure and the reviewer credibilitymeasure in the ratings list 803 and the relationship list 801respectively. In an embodiment, the operation of the credibility module240 is practical by first examining the ratings and the reviewerplausibility measure entered by the reviewers and then using the ratingcredibility measure and the reviewer credibility measure after theaggregation of the rating skill amount measure and the reviewerplausibility measure by the aggregation module. A generic computer usinga generic program cannot compute computes the reviewer credibilitymeasure of each of the experience summary data elements as disclosedabove.

The aggregation module 108 e of the experience rating and skilldiscovery system (ERSDS) 100 generates an aggregated experienceplausibility measure and an aggregated experience credibility measurecorresponding to each of the experience summary data elements in theexperience summary list 802 exemplarily illustrated in FIG. 26 , fromthe reviewer plausibility measure and the reviewer credibility measurerespectively, corresponding to each of the experience summary dataelements associated with the relationship data stored in therelationship list 801 exemplarily illustrated in FIG. 27 . As usedherein, “aggregated experience plausibility measure” refers to acombined value of the reviewer plausibility measures corresponding tomultiple occurrences of an experience summary data set in therelationship list 801. Also, as used herein, “aggregated experiencecredibility measure” refers to a combined value of the reviewercredibility measures corresponding to multiple occurrences of theexperience summary data set in the relationship list 801. Theaggregation module 108 e also generates an aggregated skill amountmeasure and an aggregated skill credibility measure corresponding toeach of the skills of the opportunity seeker in the skills profile list303 exemplarily illustrated in FIG. 25 , from the rating skill amountmeasure and the rating credibility measure respectively, correspondingto each of the skills in the ratings list 803 exemplarily illustrated inFIG. 28 . As used herein, “aggregated skill amount measure” refers to acombined value of the rating skill amount measures corresponding tomultiple occurrences of the skills in the ratings list 803. Also, asused herein, “aggregated skill credibility measure” refers to a combinedvalue of the rating credibility measures corresponding to multipleoccurrences of the skills in the ratings list 803.

The aggregation module 108 e performs a mathematical rollup of N ratingskill amount measures and rating credibility measures into a singleaggregated skill amount measure and a single aggregated skillcredibility measure respectively. The aggregation module 108 e performsa roll up of each of the multiple ratings of the skills in the ratingslist 803 exemplarily illustrated in FIG. 28 , into an aggregated skillamount measure and an aggregated skill credibility measure respectively,corresponding to a single occurrence of the skill in the skills profilelist 303 exemplarily illustrated in FIG. 25 . The aggregation module 108e also performs a roll up of the multiple reviewer plausibility measuresin the relationship list 801 exemplarily illustrated in FIG. 27 , intoan aggregated experience plausibility measure corresponding to a singleoccurrence of an experience summary data element in the experiencesummary list 802 exemplarily illustrated in FIG. 25 . The aggregatedexperience plausibility measure is similar to the aggregated skillamount measure except that the aggregated experience plausibilitymeasure applies to an experience summary data element.

The aggregation module 108 e invokes a measure aggregator 246exemplarily illustrated in FIGS. 18-19 and FIG. 21 , for generating theaggregated experience plausibility measure and the aggregated experiencecredibility measure by computing a weighted credibility measure, anaggregated unadjusted credibility measure, and a credibility bump asdisclosed in the detailed description of FIG. 20 . The aggregationmodule 108 e stores the results of the measure aggregator 246, that is,the aggregated experience plausibility measure and the aggregatedexperience credibility measure as the experience plausibility measureand the experience credibility measure respectively, corresponding toeach of the experience summary data elements in the experience summarylist 802 exemplarily illustrated in FIG. 26 .

Similarly, to generate the aggregated skill amount measure and theaggregated skill credibility measure, the aggregation module 108 einvokes the measure aggregator 246 for generating a weighted credibilitymeasure, an aggregated unadjusted credibility measure, and a credibilitybump as disclosed in the detailed description of FIG. 20 . Theaggregation module 108 e stores the results of the measure aggregator246, that is, the aggregated skill amount measure and the aggregatedskill credibility measure, as the skill amount measure and the skillcredibility measure respectively, corresponding to each of the skills inthe skills profile list 303 exemplarily illustrated in FIG. 25 . Thegenerated aggregated skill credibility measure and the generatedaggregated experience credibility measure determine credibility of theratings about the experience summary data elements and the skillsprovided by the reviewers on evaluating the experience summary dataelements and the skills associated with each of the opportunity seekers.The computed relationship depth is a factor in computing the aggregatedexperience credibility measure corresponding to each of the experiencesummary data elements in the experience summary list 802 exemplarilyillustrated in FIG. 26 , and the aggregated skill credibility measurecorresponding to each of the skills in the skills profile list 303.

The focus of the experience rating and skill discovery system (ERSDS)100 disclosed herein is on an improvement to the computer functionalityitself, and not on economic or other tasks for which a generic computeris used in its ordinary capacity. Accordingly, the ERSDS 100 disclosedherein are not directed to an abstract idea. Rather, the ERSDS 100disclosed herein is directed to a specific improvement to the way theERSDS 100 operates, embodied in, for example, generating a skillsprofile list comprising skill profiles associated with the opportunityseekers, configuring a reviewer plausibility measure corresponding toeach of the experience summary data elements in the skill profiles ofthe opportunity seekers in the relationship list, generating anaggregated experience plausibility measure and an aggregated experiencecredibility measure corresponding to each of the experience summary dataelements in the experience summary list from the reviewer plausibilitymeasure and the reviewer credibility measure respectively, generating anaggregated skill amount measure and an aggregated skill credibilitymeasure corresponding to each of the skills in said skills profile listfrom the rating skill amount measure and the rating credibility measurerespectively, etc.

FIG. 2 exemplarily illustrates a flow diagram comprising the stepsperformed by the experience rating and skill discovery system (ERSDS)for determining credibility of experience ratings provided by one ormore reviewers and discovering the skills of opportunity seekers basedon a relationship between the reviewers and the opportunity seekers. Anopportunity seeker, that is, a rated user 201 creates 203 a user profilecomprising profile data in the ERSDS 100. The skill profile module 108 aof the ERSDS 100 exemplarily illustrated in FIG. 1 , generates a skillprofile comprising skills possessed by the rated user 201 in a skillsprofile list 303 exemplarily illustrated in FIG. 25 . The rated user 201invites 204 one or more reviewers, that is, a rating user 202, and theinvitation module 108 c of the ERSDS 100 exemplarily illustrated in FIG.1 , transmits an invitation to the rating user 202 to evaluateexperience summary data sets and the skills associated with the rateduser 201 for determining plausibility of the experience summary datasets and skill amount measures of the skills associated with theexperience summary data sets and the skill profile. The rating user 202provides details of a relationship with the rated user 201 and scores205 the experience summary data elements by providing a reviewerplausibility measure corresponding to each of the experience summarydata sets. The user association module 108 b and the rating module 108 dof the ERSDS 100 exemplarily illustrated in FIG. 1 , receive and storethe relationship data in a relationship list 801 exemplarily illustratedin FIG. 27 .

The rating module 108 d of the experience rating and skill discoverysystem (ERSDS) 100 receives ratings provided by the rating user 202 forthe skills in the form of a rating skill amount measure corresponding toeach of the skills of the opportunity seeker. The rating module 108 dreceives a reviewer plausibility measure corresponding to each of theexperience summary data elements in the skill profile of the rated user201. The rating module 108 d invokes the relationship measurement module239 exemplarily illustrated in FIG. 15 , external to the ERSDS 100 tocompute a relationship depth corresponding to each of the skills. Therating module 108 d invokes the credibility module 240 exemplarilyillustrated in FIG. 16 , external to the ERSDS 100 to compute arelationship depth corresponding to each of the skills, a ratingcredibility measure of each of the opportunity seeker skill in a ratingslist 803 exemplarily illustrated in FIG. 28 , and a reviewer credibilitymeasure of each of the experience summary data elements in therelationship list 801. The aggregation module 108 e of the ERSDS 100exemplarily illustrated in FIG. 1 , computes and aggregates 106 thescores, that is, the reviewer plausibility measures and the reviewercredibility measures of the experience summary data elements asdisclosed in the detailed description of FIG. 18 . The aggregationmodule 108 e also aggregates the rating skill amount measures and therating credibility measures of the skills of the opportunity seeker asdisclosed in the detailed description of FIG. 21 .

FIG. 3 exemplarily illustrates a schematic diagram showing the skillprofile module 108 a of the experience rating and skill discovery system(ERSDS) 100 that uses profile data in the user profile list 301 and thepredefined skill list 302 to generate the skills profile list 303. Theopportunity seeker, that is, the rated user 201 provides a USER_ID, thatis, a RATED_USER_ID, and for the RATED_USER_ID, the skill profile module108 a reads the profile data in the user profile list 301 and the skillsin the predefined skill list 302 and generates or writes to the skillsprofile list 303. A web computer server 101, in communication with theuser association module 108 b exemplarily illustrated in FIG. 1 andexecuted by the processor 107 of the processing computer server 106,renders graphical user interfaces, for example, hypertext markuplanguage (HTML) screens on a seeker device 102 of the rated user 201.The HTML screens display questions regarding skills possessed by therated user 201. The user association module 108 b processes responsesprovided by the rated user 201 to the questions. The skill profilemodule 108 a generates the skills profile list 303 comprising multipleskill profiles. Each skill profile comprises a USER_ID identifying therated user 201, a skill from the predefined skill list 302, and a skillprofile name. The skill profile module 108 a renders the skills profilelist 303 on a graphical user interface of the seeker device 102. Theskill profile module 108 a pulls entries in the predefined skill list302 and renders the entries on the seeker device 102, for example, as apulldown list from which the rated user 201 selects the skills that arerelevant to the skill profile and forms a part of the skills profilelist 303. In an embodiment, a skill wizard on the seeker device 102helps render the skills profile list 303 with relevant skills. In anembodiment, the skills profile list 303 rendered on the seeker device102 is initialized by the skill wizard with a few entries comprisingpersonal traits of the rated user 201 and the remaining entries areselected by the rated user 201 from the pulldown list.

The skill profile module 108 a generates the skills profile list 303 ofthe rated users 201 with skill amount measures and skill credibilitymeasures corresponding to the skills in the skills profile list 303. Theskill amount measure is NULL and the skill credibility measure is NULLfor each of the selected skills when the skills profile list 303 isgenerated by the skill profile module 108 a. The user association module108 b receives the experience summary list 802 exemplarily illustratedin FIG. 26 , comprising experience summary data sets associated with therated users 201 in the skill profiles. The experience summary list 802is pre-existent in the database. The skill profile module 108 a onlyupdates the skill profile name corresponding to the experience summarydata elements in the experience summary list 802 to match the skillprofile name in the skills profile list 303. The aggregation module 108e updates an experience plausibility measure and an experiencecredibility measure in an experience summary data set after a rollup ofthe reviewer plausibility measures and the reviewer credibility measuresrespectively, corresponding to the experience summary data sets in therelationship list 801 exemplarily illustrated in FIG. 27 . In anembodiment, the skill profile module 108 a collects the skill profilename from a selection of the skill profiles listed on the graphical userinterfaces of the seeker devices by the rated users 201. In anotherembodiment, the skill profile module 108 a adds new skill profilescomprising new skill profile names to the skills profile list 303.

FIG. 4 exemplarily illustrates a flow diagram comprising the steps forinviting a reviewer, that is, a rating user 202 to review experiencesummary data sets and skills of an opportunity seeker, that is, therated user 201 exemplarily illustrated in FIG. 2 . The invitation module108 c of the experience rating and skill discovery system (ERSDS) 100exemplarily illustrated in FIG. 1 , transmits invitations comprisinguniform resource locator (URL) links to reviewer devices of the ratingusers 202 for evaluating experience summary data sets associated withthe rated users 201. In an embodiment, the invitations are, for example,personal text messages sent by a rated user 201 from a seeker device 102to reviewer devices of the rating users 202. The user association module108 b exemplarily illustrated in FIG. 1 , in communication with theskill profile module 108 a, creates 207 a relationship record comprisingdetails of a relationship between the rated user 201 and the rating user202 in the relationship list 801 exemplarily illustrated in FIG. 27 .The hash module, external to the ERSDS 100 and in communication with theuser association module 108 b and the invitation module, generates 208 arating user link, for example, a URL link and login credentials forrendering a graphical user interface on the reviewer device of therating user 202. The invitation module 108 c further sends 209 therating user link and the login credentials to the reviewer device of therating user 202. An operational system of an entity inserts the createdrelationship record and ensures the rating user 202 is logged into theERSDS 100. The operational system takes into account scenarios, forexample, a rating user 202 volunteering to rate a third rated user, whothe rating user 202 is aware, was involved in a job experience.

FIG. 5 exemplarily illustrates a flow diagram comprising the stepsperformed by the user association module 108 b of the experience ratingand skill discovery system (ERSDS) 100 exemplarily illustrated in FIG. 1, for creating a relationship record in the relationship list 801exemplarily illustrated in FIG. 27 . The user association module 108 bdetermines whether profile data of the rating user 202 is available inthe user profile list 301 exemplarily illustrated in FIG. 3 . If theprofile data is unavailable, the user association module 108 b, incommunication with the skill profile module 108 a exemplarilyillustrated in FIG. 3 , finds or adds 210 the rating user 202 to theuser profile list 301 with a corresponding new USER_ID based on theinputs from the rated user 201 on the seeker device 102 as exemplarilyillustrated in FIG. 6 . The rated user 201 selects 211 an experiencesummary data set associated with a skill profile from the experiencesummary list 802 exemplarily illustrated in FIG. 26 . The userassociation module 108 b creates 212 a relationship record in therelationship list 801 exemplarily illustrated in FIG. 25 . Therelationship record is an entry in the relationship list 801 identifiedby the REL_ID, comprising a RATED_USER_ID, a RATING_USER_ID, and theselected experience summary data set EXP_ID. The user association module108 b configures the reviewer plausibility measure in each relationshiprecord in the relationship list 801 as NULL.

The user association module 108 b creates 213 unfilled ratings in theratings list 803 exemplarily illustrated in FIG. 28 . In the ratingslist 803, the user association module 108 b determines the skill profilename in a relationship record identified by the REL_ID in therelationship list 801 exemplarily illustrated in FIG. 27 , from theskill profile name in the experience summary data set identified by theEXP_ID in the experience summary list 802 exemplarily illustrated inFIG. 26 . The user association module 108 b then inserts a rowidentified by the RATED_USER_ID from the user profile list 301 andidentified by the skill profile name for each of the skills of theopportunity seeker in the skill profiles that are identified by theRATED_USER_ID from the user profile list 301 and the skill profile name,into the ratings list 803 exemplarily illustrated in FIG. 28 . That is,the user association module 108 b inserts a row for each of the skillsin the skill profiles in the skills profile list 303, into the ratingslist 803, where the USER_ID in the skill profiles is the same as theRATED_USER_ID and the skill profile name in the skill profiles is thesame as the determined skill profile name in the relationship recordwith a corresponding REL_ID. The row further comprises the createdrelationship record with the corresponding REL_ID from the relationshiplist 801, and NULL values for a rating skill amount measureAMOUNT_PRESENT and a strength of belief measure STRENGTH OF BELIEF asexemplarily illustrated in FIG. 26 . The unfilled rows, that is, therows with NULL values in the ratings list 803 await values from therating user 202. In an embodiment, the ratings are added to the ratingslist 803 only when the rating user 202 uses the experience rating andskill discovery system (ERSDS) 100 and supplies ratings.

The user association module 108 b, in communication with the databaseserver 104 as exemplarily illustrated in FIG. 1 , is invoked with thecreated relationship record with the corresponding REL_ID, and interactswith the rated user 201 to collect additional details about anexperience summary data set corresponding to an EXP_ID as exemplarilyillustrated in FIG. 8 . The rated user 201 supplies 214 relationshipdetails about the created relationship record to the user associationmodule 108 b in the form of responses to questions posed by the userassociation module 108 b. The user association module 108 b calls therelationship measurement module 239 exemplarily illustrated in FIG. 15 ,to collect additional details as determined by the relationshipmeasurement module 239. The relationship measurement module 239, onreview of the responses to the questions, assigns a relationship depthbut not reviewer credibility measures to the created relationship recordin the relationship list 801 exemplarily illustrated in FIG. 27 .Furthermore, the relationship measurement module 239, on review of theresponses to the questions, makes a judgment on whether a particularworking relationship of the rated user 201 in a skill profile isjustified, for example, whether reviews of the hypertext preprocessor(PHP) programming language by a rating user 202 are justified or notjustified. The rating module 108 d exemplarily illustrated in FIG. 1 ,updates the records in the ratings list 803 exemplarily illustrated inFIG. 28 , with the computed relationship depth but does not update therating credibility measure corresponding to the records in the ratingslist 803. The relationship measurement module 239, knowing substantiallymore about the relationship between the rated user 201 and the ratinguser 202, makes judgments.

FIG. 6 exemplarily illustrates a flowchart comprising the stepsperformed by the user association module 108 b exemplarily illustratedin FIG. 8 , for finding or adding a reviewer, that is, a rating user 202to the user profile list 301 exemplarily illustrated in FIG. 24 . Theweb computer server 101 of the experience rating and skill discoverysystem (ERSDS) 100 exemplarily illustrated in FIG. 1 , renders graphicaluser interfaces 101 a on the seeker devices 102 and the reviewer devices103. The graphical user interfaces 101 a are web based interfaces, forexample, hypertext markup language (HTML) screens. The rated user 201enters 215 profile data comprising, for example, a first name, a lastname, and an electronic mail (email) address of the rating user 202 onthe HTML screens rendered on the seeker devices. The user associationmodule 108 b searches 216 for a user profile in the user profile list301 for a match with the entered profile data by the rated user 201. Ifthe user association module 108 b finds 217 an exact match of the firstname, the last name, and the email address of the rating user 202 with arecord in the user profile list 301, the user association module 108 bassigns the USER_ID corresponding to the matching record as the USER_IDof the rating user 202. If the user association module 108 b does notfind 217 an exact match of the first name, the last name, and the emailaddress of the rating user 202 with a record in the user profile list301, the skill profile module 108 a exemplarily illustrated in FIG. 1 ,inserts 218 a new record in the user profile list 301 comprising thefirst name, the last name, and the email address entered by the ratinguser 202 with a new USER_ID. In an embodiment, the user associationmodule 108 b performs a partial match of the first name, the last name,and the email address of the rating user 202 with a user profile in theuser profile list 301.

FIG. 7 exemplarily illustrates a flowchart comprising the steps forselecting an experience summary data set with a corresponding skillprofile name from the experience summary list 802 exemplarilyillustrated in FIG. 26 , by an opportunity seeker, that is, a rated user201. The web computer server 101 renders graphical user interfaces 101a, for example, webpages or hypertext markup language (HTML) screens onthe seeker device 102. The webpages display the experience summary datasets in the experience summary list 802 to allow the rated user 201 toselect 219 EXP_IDs and experience summary data sets from the experiencesummary list 802 for a rated user 201, where USER_ID=RATED_USER_ID. Theuser association module 108 b exemplarily illustrated in FIG. 1 ,displays 220 experience summary data sets associated with the rated user201 and allows one of the experience summary data sets to be selected.The user association module 108 b determines whether the experiencesummary data set is associated with a skill profile name. That is, theuser association module 108 b determines 221 whether the skill profilename for the experience summary data element is set. If the skillprofile name is set, the user association module 108 b displays theselected experience summary data set with the skill profile name. If theselected experience summary data set does not have a corresponding skillprofile name, the user association module 108 b presents 222 a list ofdistinct skill profile names from the skills profile list 303exemplarily illustrated in FIG. 25 , with corresponding USER_IDs on theseeker device 102. The rated user 201 selects one of the skill profilenames from the provided list of distinct skill profile names, and theuser association module 108 b saves the selected skill profile name inthe experience summary list 802 corresponding to the selected experiencesummary data set.

FIG. 8 exemplarily illustrates a schematic diagram showing the userassociation module 108 b that collects the relationship data from anopportunity seeker, that is, the rated user 201, about a relationshiprecord created in the relationship list 801. The created relationshiprecord has a corresponding REL_ID that is passed to the relationshipmeasurement module 239 exemplarily illustrated in FIG. 15 . The userassociation module 108 b receives and stores the relationship data, forexample, in the form of responses to questions from the rated user 201and transmits the responses to the relationship measurement module 239.The user association module 108 b communicates with the database server104 over the network 105 and accesses the user profile list 301exemplarily illustrated in FIG. 24 , the skills profile list 303exemplarily illustrated in FIG. 3 , the experience summary list 802, therelationship list 801, and the ratings list 803. In an embodiment, theuser association module 108 b maintains a local version of the userprofile list 301, the skills profile list 303, the experience summarylist 802, the relationship list 801, and the ratings list 803. Inanother embodiment, the user association module 108 b maintains a localversion of the user profile list 301, the skills profile list 303, theexperience summary list 802, the relationship list 801, and the ratingslist 803 in non-relational data stores. The user association module 108b poses questions, for example, “Were they your manager: 1” to the rateduser 201 on the seeker device and receives responses to the questions.The relationship measurement module 239, in communication with the userassociation module 108 b, interacts with the rated user 201 via thegraphical user interfaces 101 a, for example, webpages rendered on theseeker device 102 to learn more about the relationship between the rateduser 201 and the rating user 202. The user association module 108 binteracts with the rated user 201 via the seeker device 102 usingdifferent modes of interactions, for example, questions such as “Howlong did you work with them?” or “Did one of you supervise the other?”.In an embodiment, the user association module 108 b uses existingrelationships in the relationship list 801 to refine the questions askedto the rated user 201, for example, “Did you also work with Mary?”

The rating module 108 d exemplarily illustrated in FIG. 1 , interactswith a rating user 202 as exemplarily illustrated in FIG. 10 , usingdifferent modes of interactions, for example, questions and answers. Theresponses of the rating user 202 and the rated user 201 to the questionsposed are used by the relationship measurement module 239 to compute arelationship depth of the relationship between the rating user 202 andthe rated user 201.

FIG. 9 exemplarily illustrates a flow diagram comprising the stepsperformed by the hash module for generating a uniform resource locator(URL) link, that is, a rating user link for rendering graphical userinterfaces 101 a on a reviewer device 103. The rating user 202exemplarily illustrated in FIG. 2 , uses the rating user link toinitiate an interaction with the experience rating and skill discoverysystem (ERSDS) 100 to rate a rated user 201 exemplarily illustrated inFIG. 2 . The REL_ID corresponding to a relationship between the rateduser 201 and the rating user 202 is input to the hash module and thehash module encodes and encrypts the REL_ID in a manner acceptable tothe ERSDS, for example, by creating 223 a hash value such as13691c2BB680F8T1526455884. The encrypted hash value is then added 224 toa prototype link to create a URL link, for example,http://thesystem.com?rate=13691c2BB680F8T1526455884, that is used tointeract with the ERSDS as the rating user 202. In an embodiment, thehash module returns the REL_ID. In this embodiment, the URL linkgenerated is, for example, http://thesystem.com?rel_id=2. In anembodiment, the URL link is sufficient to log in the rating user 202. Inan embodiment, the rating user 202 also receives additional logincredentials as a part of the invitation from the skill profile module108 a exemplarily illustrated in FIG. 1 . The rating user 202 logs inwith the login credentials in a manner similar to logging in from asocial networking website.

The invitation module 108 c in communication with the rated user 201transmits invitations comprising the rating user link to the rating user202. In an embodiment, the invitation module 108 c displays thegenerated rating user link to the rated user 201 in the graphical userinterfaces 101 a of the seeker device 102 and offers to transmit theinvitation to the rated user 201, for example, via an electronic mail(email). The invitation module 108 c composes an email based on atemplate with information of the rated user 201 and the rating user 202,for example, the email addresses of the rated user 201 and the ratinguser 202 and transmits the generated rating user link as a body of theemail to the email address of the rating user 202.

FIG. 10 exemplarily illustrates a flow diagram comprising the stepsperformed by the rating module 108 d exemplarily illustrated in FIG. 1 ,for receiving ratings corresponding to each of the skills associatedwith the experience summary data elements and a reviewer plausibilitymeasure corresponding to each of the experience summary data elementsfrom the reviewers and the steps performed by the relationshipmeasurement module 239 and the credibility module 240 exemplarilyillustrated in FIG. 15 and FIG. 16 respectively, external to theexperience rating and skill discovery system (ERSDS) 100 for scoring thereceived ratings and the relationship data. The rating user 202 enters225 a review in the form of responses to questions posed by the ratingmodule 108 d on the graphical user interfaces 101 a of the reviewerdevice 103 as disclosed in the detailed description of FIG. 8 . Therelationship measurement module 239 evaluates the responses and scores226 the relationship by computing a relationship depth of a relationshipbetween the rating user 202 and the rated user 201 exemplarilyillustrated in FIG. 2 , per skill possessed by the rated user 201. In anembodiment, the relationship depth is received as part of the ratingscomprising the rating skill amount measure and the strength of beliefmeasure. The rating module 108 d alters the relationship depth in theratings list 803 exemplarily illustrated in FIG. 28 , based on theresponses provided by the rating user 202. The credibility module 240 isinvoked by the rating module 108 d to score 227 the received ratingskill amount measure and the received strength of belief measurecorresponding to each of the skills and the reviewer plausibilitymeasure corresponding to each of the experience summary data elementsbased on the relationship depths computed by the relationshipmeasurement module 239. The credibility module 240 computes a ratingcredibility measure of each of the skills and a reviewer credibilitymeasure of each of the experience summary data elements.

FIG. 11 exemplarily illustrates a flow diagram comprising the steps forproviding the ratings corresponding to each of the skills associatedwith the experience summary data sets of an opportunity seeker, that is,the rated user 201, by a reviewer, that is, a rating user 202exemplarily illustrated in FIG. 2 . The rating user 202 receives 228 therating user link, for example, by either an electronic mail (email) fromthe invitation module of the experience rating and skill discoverysystem (ERSDS) or by a text message sent from the seeker device of therated user 201. The rating user link comprises the REL_ID correspondingto a relationship between the rated user 201 and the rating user 202 inan encrypted computer readable form. On clicking the rating user link,graphical user interfaces 101 a are rendered on the reviewer device 103for the rating user 202 to provide the ratings. When the rating user 202browses 229 the rating user link, a web session is created that cachesthe REL_ID for the remainder of the web session. From the REL_ID, therating module 108 d exemplarily illustrated in FIG. 1 , determines therated user 201, the rating user 202, and the experience summary datasets identified by the EXP_ID involved in the relationship identified bythe REL_ID. The rating module 108 d presents the rating user 202 withquestions regarding the relationship with the rated user 201. The ratinguser 202 answers 230 the questions via the graphical user interfaces,for example, webpages rendered on the reviewer device where the ratinguser 202 rates 231 the experience summary data elements of theexperience summary data sets identified by the EXP_ID. The rating user202 provides the ratings via the graphical user interfaces 101 a, forexample, webpages rendered on the reviewer device 103 where the ratinguser 202 rates 232 the skills possessed by the opportunity seekers 201.In an embodiment, steps 230, 231, and 232 are performed in differentsequences.

In an embodiment, if the rating user 202 is already logged into theexperience rating and skill discovery system (ERSDS) 100 as a rated user201 or as a rating user 202 who rates another rated user 201, the ratinguser 202 navigates to a graphical user interface 101 a listing all theexperience summary data sets to be reviewed by the rating user 202 basedon received invitations to rate. The rating user 202 is allowed toselect one of the experience summary data sets to rate and the stepsdisclosed in the detailed description of FIG. 11 are executed. In anembodiment, the rating user 202, if interacting for the first time withthe ERSDS 100, is requested to provide login credentials, for example, apassword by the rating module 108 d. If the rating user 202 isinteracting with the ERSDS 100 for a subsequent time, the rating module108 d requests the rating user 202 for the login credentials and thelogin credentials are checked before initiating a session as the ratinguser 202 in the ERSDS 100.

FIG. 12 exemplarily illustrates a schematic diagram showing the ratingmodule 108 d that invokes a relationship measurement module 239 tocompute and send the relationship depth of the relationship record inthe relationship list 801. The rating module 108 d caches the responsesprovided by the rated user 201 for the questions posed as disclosed inthe detailed description of FIG. 8 , and the relationship measurementmodule 239 exemplarily illustrated in FIG. 15 , uses the responses fromthe rated user 201 in selecting questions to be posed by the ratingmodule 108 d to the rating user 202 regarding the relationship with therated user 201. If the user association module 108 b exemplarilyillustrated in FIG. 1 , for example, receives a first response from therated user 201 as “I don't know”, the rating module 108 d poses morequestions to the rating user 202 that should have been answered by therated user 201. Furthermore, if the user association module 108 b, forexample, receives a first response from the rated user 201, the ratingmodule 108 d poses more questions to the rating user 202 to checkconsistency of the responses provided by the rated user 201 to thequestions. The rating module 108 d passes the REL_ID corresponding tothe relationship between the rated user 201 and the rating user 202 tothe relationship measurement module 239.

The rating module 108 d communicates with the database server over thenetwork and accesses the user profile list 301, the skills profile list303 exemplarily illustrated in FIG. 3 , the experience summary list 802,the relationship list 801, and the ratings list 803. The rating module108 d interacts with the rating user 202 via the reviewer device usingdifferent modes of interactions, for example, questions such as “Howlong did you work with them?” or “Did one of you supervise the other?”.In an embodiment, the rating module 108 d, in communication with therelationship measurement module 239, uses existing relationships in therelationship list 801 to refine the questions asked to the rating user202, for example, “Did you also work with Mary?”. In an embodiment,profile data of the rated user 201, for example, a resume claim of therated user 201, is a part of the questions posed to the rating user 202.The relationship measurement module 239 computes and sets therelationship depth of the relationship record in the relationship list801 with the matching REL_ID and each skill corresponding to the REL_IDin the ratings list 803. The relationship measurement module 239, forexample, determines that the relationship between the rated user 201 andthe rating user 202 is not deep in regard to knowledge of the hypertextpreprocessor (PHP) programming language skills, but is deep forknowledge of customer relations skills. The relationship measurementmodule 239 sets the relationship depths for PHP programming skillscorresponding to the REL_ID in the ratings list 803.

FIG. 13 exemplarily illustrates a flow diagram comprising the stepsperformed by the rating module 108 d exemplarily illustrated in FIG. 12, for receiving and configuring a reviewer plausibility measure of anexperience summary data set from a reviewer device 103. For arelationship identified by the REL_ID in the relationship list 801, therating module 108 d fetches 233 the relationship data, the experiencesummary data elements, and the profile data of the rated user 201exemplarily illustrated in FIG. 2 , from the relationship list 801, theexperience summary list 802, and the user profile list 301 respectively,exemplarily illustrated in FIG. 12 . The rating module 108 d presents234 graphical user interfaces 101 a, for example, webpages or webscreens on the reviewer device 103 of the reviewer, that is, the ratinguser 202. The graphical user interfaces 101 a comprise informationidentifying the rated user 201 and the experience summary data elements.The rating user 202 makes a selection of a numerical value thatindicates plausibility of the experience summary data element beingassociated with the rated user 201, in the opinion of the rating user202, on the graphical user interface 101 a. The selection on thegraphical user interface 101 a is a direct numerical entry or any of themany user interface techniques comprising, for example, pulldown lists,stars, etc. The rating module 108 d saves and configures 235 thenumerical selection as a reviewer plausibility measure in a row of therelationship list 801 corresponding to the given REL_ID.

FIG. 14 exemplarily illustrates a flow diagram comprising the stepsperformed by the rating module 108 d exemplarily illustrated in FIG. 1 ,for receiving ratings corresponding to skills in the skills profile list303 exemplarily illustrated in FIG. 3 , from a reviewer device 103. Fora relationship identified by the REL_ID, the rating module 108 d fetches236 the relationship data, the experience summary data elements, theprofile data of the rated user 201 exemplarily illustrated in FIG. 2 ,and the ratings from the relationship list 801, the experience summarylist 802, the user profile list 301, and the ratings list 803respectively, exemplarily illustrated in FIG. 12 . The rating module 108d presents 237 graphical user interfaces 101 a, for example, webpages orweb screens on the reviewer device 103 of the reviewer, that is, therating user 202. The graphical user interfaces 101 a compriseinformation identifying the rated user 201 and a list of skillspossessed by the rated user 201 present in the skills profile list 303.The list of the skills comprises the skills from the ratings list 803corresponding to the REL_ID given in the rating user link. In thegraphical user interfaces 101 a, the rating module 108 d provides, foreach skill in the list of skills, a container to enter the rating skillamount measure and the strength of belief measure. In an embodiment, therating user 202 enters the rating skill amount measure and the strengthof belief measure, for example, via stars or pulldown lists for each ofthe skills in the list of skills. In an embodiment, the rating skillamount measure and the strength of belief measure of the skills are setto a predefined default value, that is, rarely altered by the ratinguser 202. The rating user 202 interacts with the graphical userinterfaces 101 a, for example, webpages to supply ratings for some ofthe skills. The rating module 108 d receives and updates 238 the ratingskill amount measure and the strength of belief measure in the ratingslist 803 corresponding to the skills without affecting the values forthe relationship depth and the rating credibility measure.

FIG. 15 exemplarily illustrates a schematic diagram showing therelationship measurement module 239 that computes a relationship depthof a relationship between an opportunity seeker, that is, the rated user201 exemplarily illustrated in FIG. 2 , and a reviewer, that is, therating user 202. The relationship measurement module 239 is invoked witha REL_ID identifying the relationship between the rated user 201 and therating user 202. The relationship measurement module 239 computes therelationship depth of the relationship and updates the computedrelationship depth in the relationship list 801 and the ratings list803. The relationship measurement module 239 computes the relationshipdepth based on the responses received from the rated user 201 and therating user 202 as disclosed in the detailed description of FIG. 8 andFIG. 12 . In an embodiment, the relationship measurement module 239computes the relationship depth based on the responses from other ratedusers 201 and other rating users 202 who are relevant to therelationship between the rated user 201 and the rating user 202. In anembodiment, the relationship measurement module 239 applies skillspecific adjustments, for example, awareness that a rating user 202 hasa high relationship depth to rate Microsoft® Office skills but has a lowrelationship depth to rate hypertext preprocessor (PHP) code developmentskills. Based on the computed relationship depth, the relationshipmeasurement module 239 lowers a previously assigned relationship depthwhen some of the skills expected to be rated by the rating user 202remain unrated. At the end of the processing by the relationshipmeasurement module 239, the relationship depth in the relationship list801 and the relationship depth in the ratings list 803 are set.

FIG. 16 exemplarily illustrates a schematic diagram showing thecredibility module 240 that computes and stores rating credibilitymeasures of the skills. The credibility module 240 is invoked with theREL_ID. The credibility module 240 considers the relationship list 801and the ratings list 803, including the relationship depth and thereviewer plausibility measure. The credibility module 240 computes andupdates the rating credibility measure associated with the REL_ID in theratings list 803. The credibility module 240 updates the reviewercredibility measure associated with the REL_ID in the relationship list801. In an embodiment, the credibility module 240 presumes that when allof the ratings provided by a rating user 202 are high, then the ratingsfrom the rating user 202 will have a low credibility measure. In anotherembodiment, the credibility module 240 alters the reviewer credibilitymeasure and the rating credibility measure based on responses from otherrated users 201 and other rating users 202 who are relevant to therelationship between the rated user 201 exemplarily illustrated in FIG.2 , and the rating user 202. In an embodiment, the credibility module240 applies skill specific adjustments, for example, awareness that arating user 202 has a high relationship depth to rate Microsoft® Officeskills with a high rating credibility measure, but has a lowrelationship depth to rate hypertext preprocessor (PHP) code developmentskills, and any such ratings are to have low credibility measures. In anembodiment, the rating users 202 who have rated other rated users 201with a high reviewer credibility measure will have the ratings assigneda higher credibility measure than the ratings the rating users 202 willotherwise receive. At the end of the processing by the credibilitymodule 240, the reviewer credibility measure in the relationship list801 for the relationship between the rated user 201 and the rating user202 and the rating credibility measure in the ratings list 803 for theskills are set.

The rating module 108 d exemplarily illustrated in FIG. 1 , updates therelationship depths in the ratings list 803 and the relationship list801 after receiving ratings from the rating user 202. After all theratings have been provided, the rating module 108 d calls therelationship measurement module 239 exemplarily illustrated in FIG. 15 ,again where the rating module 108 d cannot ask further questions but mayupdate the ratings in the ratings list 803. The rating module 108 d, incommunication with the relationship measurement module 239, based on theratings received, does not pose questions to the rating user 202regarding the skill if the relationship between the rated user 201 andthe rating user 202 is weak. That is, the relationship measurementmodule 239 determines that the lack of ratings for certain skillsimplies a weaker relationship between the rated user 201 and the ratinguser 202 than previously determined, and the relationship measurementmodule 239 determines that the ratings for other skills possessed by therated user 201 provided by the rating user 202 are unjustified by therelationship between the rated user 201 and the rating user 202. Forexample, if the rating user 202 skips to provide all the ratings in theratings list 803, then the rating module 108 d, in communication withthe relationship measurement module 239, may conclude that therelationship did not go that deep and is weak and may not pose questionsto the rating user 202.

FIG. 17 exemplarily illustrates flow diagrams comprising the stepsperformed by the aggregation module 108 e of the experience rating andskill discovery system (ERSDS) 100 exemplarily illustrated in FIG. 1 ,for aggregating the relationship data and ratings. The aggregationmodule 108 e aggregates the reviewer plausibility measure and thereviewer credibility measure for each of the experience summary dataelements associated with the relationship records in the relationshiplist 801. The aggregation module 108 e also aggregates the rating skillamount measure and the rating credibility measure corresponding to eachof the skills in the ratings list 803. The aggregation module 108 e isinvoked at any time, for example, after each review of the experiencesummary data sets and the skills, or on demand, or as batch runs. For anexperience summary data set 241 identified by the EXP_ID in theexperience summary list 802 exemplarily illustrated in FIG. 8 , theaggregation module 108 e computes 242 an aggregated experienceplausibility measure and an aggregated experience credibility measureand updates these aggregated measures as an experience plausibilitymeasure and an experience credibility measure respectively, in theexperience summary data set 241 identified by the EXP_ID in theexperience summary list 802. For each skill 243 in the skills profilelist 303 exemplarily illustrated in FIG. 3 , possessed by a rated user201 exemplarily illustrated in FIG. 2 , with the USER_ID in a skillprofile, the aggregation module computes 244 an aggregated skill amountmeasure and an aggregated skill credibility measure and updates theseaggregated measures as a skill amount measure and a skill credibilitymeasure respectively, in the skills profile list 303.

FIG. 18 exemplarily illustrates a flow diagram comprising the stepsperformed by the measure aggregator 246 invoked by the aggregationmodule 108 e for computing an aggregated experience plausibility measureand an aggregated experience credibility measure for a single experiencesummary data set. The measure aggregator 246 is invoked by theaggregation module 108 e with a reviewer plausibility measure and areviewer credibility measure corresponding to an experience summary dataset identified by the EXP_ID. The aggregation module 108 e fetches 245the reviewer plausibility measures and the reviewer credibility measuresfor the relationship records with the EXP_ID. The fetched reviewerplausibility measures and the fetched reviewer credibility measures arepassed to the measure aggregator 246 and the measure aggregator 246returns a tuple comprising an aggregated experience plausibility measureand an aggregated experience credibility measure respectively, asexemplarily illustrated in FIG. 19. The aggregation module 108 e updates247 the experience plausibility measure and the experience credibilitymeasure corresponding to the experience summary data set identified bythe EXP_ID in the experience summary list 802 exemplarily illustrated inFIG. 7 , with the aggregated experience plausibility measure and theaggregated experience credibility measure respectively.

FIG. 19 exemplarily illustrates a flow diagram comprising the stepsperformed by the measure aggregator 246 for computing an aggregatedexperience plausibility measure or an aggregated skill amount measureand an aggregated experience credibility measure or an aggregated skillcredibility measure. The measure aggregator 246 is invoked with a listof tuples, where one element of a tuple in the list is the rating skillamount measure or the reviewer plausibility measure to aggregate and theother element of the tuple in the list is the rating credibility measureor the reviewer credibility measure. The measure aggregator 246 computesand returns a single tuple with an aggregated measure, that is, theaggregated skill amount measure or the aggregated experienceplausibility measure and the aggregated skill credibility measure or theaggregated experience credibility measure respectively.

FIG. 20 exemplarily illustrates a flow diagram comprising the stepsperformed by the measure aggregator 246 for generating the aggregatedexperience credibility measure and the aggregated experienceplausibility measure. The measure aggregator 246 is invoked with anynumber of tuples comprising reviewer plausibility measures and reviewercredibility measures corresponding to the experience summary data setsidentified by the EXP_ID in the relationship list 801. The measureaggregator 246 computes 248 the aggregated experience plausibilitymeasure as Sum (reviewer plausibility measure*reviewer credibilitymeasure)/Sum (reviewer credibility measure). The measure aggregator 246computes 249 an aggregated unadjusted credibility measure as Sum(reviewer credibility measure*reviewer credibility measure)/Sum(reviewer credibility measure). The measure aggregator 246 computes 250a credibility bump as Sum (reviewer credibility measure*reviewercredibility measure)*coeff_credbump−coeff_credbump, where coeff_credbumpis a system constant. The measure aggregator 246 computes 251 anaggregated experience credibility measure as aggregated unadjustedcredibility measure+credibility bump but then adjusted to not be lowerthan a predefined minimum value or larger than a predefined maximumvalue. The aggregated experience plausibility measure and the aggregatedexperience credibility measure are then returned as a tuple. In anembodiment, the measure aggregator 246 computes the credibility bump asa lookup of the sum of the reviewer credibility measure against apredefined credibility bump lookup table. The foregoing formulas havebeen provided merely for explanation and are in no way to be construedas limiting the step of generating the aggregated experience credibilitymeasure and the aggregated experience plausibility measure disclosedherein.

FIG. 21 exemplarily illustrates a flow diagram comprising the stepsperformed by the aggregation module 108 e exemplarily illustrated inFIG. 1 , for computing an aggregated skill amount measure and anaggregated skill credibility measure for a single skill in the skillsprofile list 303 exemplarily illustrated in FIG. 3 . The aggregationmodule 108 e is invoked with a USER_ID, a skill, and a skill profilename. The aggregation module 108 e scans through the skills profile list303 and for each skill in the skills profile list 303, the aggregationmodule fetches 252, from the ratings list 803 exemplarily illustrated inFIG. 8 , a rating skill amount measure and a rating credibility measurefor the skill with the REL_ID of an experience summary data setcomprising the same USER_ID and the same skill profile name. Theaggregation module 108 e makes a list of all the rating skill amountmeasures and the rating credibility measures from the ratings list 803for a combination of the USER_ID, the skill, and the skill profile namein the skills profile list 303.

A pseudocode defined by the aggregation module 108 e executable by atleast one processor 107 of the processing computer server 106 of theexperience rating and skill discovery system (ERSDS) 100 for fetchingthe rating skill amount measures and the rating credibility measuresfrom the ratings list 803 for a combination of the USER_ID, a skill, anda skill profile name from the ratings list 803 is disclosed below:

-   -   SELECT AMOUNT_PRESENT, credibility FROM ratings WHERE        skill=$skill    -   AND REL_ID_IN (SELECT REL_ID FROM relationship    -   WHERE EXP_ID_IN (SELECT EXP_ID from job_experience where    -   USER_ID=$USER_ID and profile_name=$profile_name))

The measure aggregator 246 works on the fetched rating skill amountmeasures and the rating credibility measures and returns a tuplecomprising a single aggregated skill amount measure and a singleaggregated skill credibility measure. The aggregation module 108 eupdates 253 the skill amount measure and the skill credibility measurecorresponding to the skills in the skills profile list 303 with theaggregated skill amount measure and the aggregated skill credibilitymeasure respectively.

FIGS. 22A-22C exemplarily illustrates a method for determiningcredibility of experience ratings provided by one or more reviewers anddiscovering skills of opportunity seekers based on a relationshipbetween the reviewers and the opportunity seekers. The method disclosedherein employs an experience rating and skill discovery system (ERSDS)100 comprising at least one processor 107 configured to execute computerprogram instruction for determining credibility of experience ratingsprovided by one or more reviewers and discovering skills of opportunityseekers based on a relationship between the reviewers and theopportunity seekers as disclosed in detailed description of FIG. 1 . TheERSDS 100 reads 2201 profile data of the opportunity seekers stored in auser profile list 301 exemplarily illustrated in FIG. 24 . The ERSDS 100generates 2202 a skills profile list 303 exemplarily illustrated in FIG.25 , comprising skill profiles associated with the opportunity seekersusing the profile data and the skills selected from a predefined skilllist 302 exemplarily illustrated in FIG. 23 , via the seeker devices102. The skill profiles comprise the skills of the opportunity seekerswith corresponding skill amount measures and corresponding skillcredibility measures indicating credibility of the skill amountmeasures. The ERSDS 100 receives 2203 an experience summary list 802exemplarily illustrated in FIG. 26 , comprising experience summary dataelements in the skill profiles listed in the skills profile list 303exemplarily illustrated in FIG. 25 , of the opportunity seekers withcorresponding experience plausibility measures and correspondingexperience credibility measures. The ERSDS 100 transmits 2204invitations to the reviewer devices 103 for evaluating the experiencesummary data elements in the received experience summary list 802exemplarily illustrated in FIG. 26 , and the skills of the opportunityseeker in the generated skills profile list 303 exemplarily illustratedin FIG. 25 . The evaluation of the experience summary data elements andthe skills of the opportunity seekers allow the reviewers to discoverthe skills possessed by an opportunity seeker in a skill profileassociated with the experience summary data elements in the experiencesummary list 802.

The experience rating and skill discovery system (ERSDS) 100 determines2205 whether profile data of the reviewers with the transmittedinvitations is available in the user profile list 301 and receives andstores unavailable profile data of the reviewers with the transmittedinvitations in the user profile list 301 exemplarily illustrated in FIG.24 . The ERSDS 100 configures 2206 a reviewer plausibility measurecorresponding to each of the experience summary data elements in theskill profiles of the opportunity seekers in a relationship list 801exemplarily illustrated in FIG. 27 . The ERSDS 100 collects 2207relationship data comprising information of relationships between eachof the reviewers and each of the opportunity seekers and stores thecollected relationship data in a relationship list 801. The ERSDS 100receives 2208 a reviewer plausibility measure corresponding to each ofthe experience summary data elements in the skill profiles of theopportunity seekers from the reviewer devices 103 and updating thereceived reviewer plausibility measure in the relationship list 801, asdisclosed in detailed description of FIG. 1 .

The experience rating and skill discovery system (ERSDS) 100 receives2209 ratings provided by each of the reviewers from the reviewer deviceson evaluating said skills associated with the experience summary dataelements in the skill profiles, and for updating the ratings listcomprising a computed relationship depth corresponding to each of theskills. The ratings comprise a rating skill amount measure and astrength of belief measure for each of the skills. The computedrelationship depth is a factor in computing an aggregated experiencecredibility measure corresponding to each of the experience summary dataelements in the experience summary list 802 exemplarily illustrated inFIG. 26 , and an aggregated skill credibility measure corresponding toeach of the skills in the skills profile list. The ERSDS 100 receives2210 a reviewer credibility measure of each of the experience summarydata elements in the skill profiles of the opportunity seekers andstores the reviewer credibility measure in the relationship list 801.The reviewer credibility measure indicates credibility of the reviewerplausibility measure corresponding to each of the experience summarydata elements in the skill profiles of the opportunity seekers. TheERSDS 100 receives 2211 a rating credibility measure of each of theskills and stores the rating credibility measure in the ratings list 803exemplarily illustrated in FIG. 28 . The rating credibility measureindicates credibility of the rating skill amount measure of each of theskills.

The experience rating and skill discovery system (ERSDS) 100 generates2212 an aggregated experience plausibility measure and the aggregatedexperience credibility measure corresponding to each of the experiencesummary data elements in the experience summary list 802 exemplarilyillustrated in FIG. 26 , from the reviewer plausibility measure and thereviewer credibility measure respectively, corresponding to each of theexperience summary data elements associated with the relationship datastored in said relationship list 801 by computing a weighted credibilitymeasure, an aggregated unadjusted credibility measure, and a credibilitybump and stores the generated aggregated experience plausibility measureand the generated aggregated experience credibility measure as theexperience plausibility measure and the experience credibility measurecorresponding to each of the experience summary data elements in theexperience summary list 802 as disclosed in detailed description of FIG.1 . The ERSDS 100 generates 2213 an aggregated skill amount measure andthe aggregated skill credibility measure corresponding to each of theskills of the opportunity seeker in the skills profile list 303exemplarily illustrated in FIG. 25 , from the rating skill amountmeasure and the rating credibility measure respectively, correspondingto each of the skills in the ratings list 803 exemplarily illustrated inFIG. 28 , and stores the generated aggregated skill amount measure andthe aggregated skill credibility measure as a skill amount measure and askill credibility measure corresponding to each of the skills in theskills profile list 303 as disclosed in detailed description of FIG. 1 .The generated aggregated skill credibility measure and the generatedaggregated experience credibility measure determine credibility ofexperience ratings provided by the reviewers on evaluating theexperience summary data elements and the skills associated with each ofthe opportunity seekers as disclosed in detailed description of FIG. 1 .

The computer program codes embodied in the non-transitory computerreadable storage medium comprise a first computer program code forreading profile data of the opportunity seekers stored in a user profilelist; a second computer program code for generating a skills profilelist comprising skill profiles associated with the opportunity seekersusing the profile data and the skills selected from a predefined skilllist via the seeker devices, wherein the skill profiles comprise theskills with corresponding skill amount measures and corresponding skillcredibility measures indicating credibility of the skill amountmeasures; a third computer program code for receiving an experiencesummary list comprising experience summary data elements in the skillprofiles of the opportunity seekers with corresponding experienceplausibility measures and corresponding experience credibility measures;a fourth computer program code for transmitting invitations to thereviewer devices for evaluating said experience summary data elements inthe received experience summary list and the skills of the opportunityseekers in said generated skills profile list, thereby allowing thereviewers to discover the skills possessed by an opportunity seeker in askill profile associated with said experience summary data elements insaid experience summary list; a fifth computer program code fordetermining whether profile data of the reviewers with the transmittedinvitations is available in the user profile list and receiving andstoring unavailable profile data of the reviewers with the transmittedinvitations in the user profile list; a sixth computer program code forconfiguring a reviewer plausibility measure corresponding to each of theexperience summary data elements in the skill profiles of theopportunity seekers in a relationship list; a seventh computer programcode for collecting relationship data comprising information ofrelationships between each of the one or more reviewers and each of theopportunity seekers and storing the collected relationship data in arelationship list; a eighth computer program code for receiving areviewer plausibility measure corresponding to each of the experiencesummary data elements in the skill profiles of the opportunity seekersfrom the reviewer devices and updating the received reviewerplausibility measure in the relationship list; a ninth computer programcode for receiving ratings provided by each of the reviewers from thereviewer devices on evaluating said skills associated with theexperience summary data elements in the skill profiles, and for updatingthe ratings list comprising a computed relationship depth correspondingto each of the skills, wherein the ratings comprise a rating skillamount measure and a strength of belief measure for each of the skills,and wherein the computed relationship depth computed is a factor incomputing an aggregated experience credibility measure corresponding toeach of the experience summary data elements in the experience summarylist and an aggregated skill credibility measure corresponding to eachof the skills in the skills profile list; a tenth computer program codefor receiving a reviewer credibility measure of each of the experiencesummary data elements in the skill profiles of the opportunity seekersand storing said reviewer credibility measure in the relationship list,wherein the reviewer credibility measure indicates credibility of saidreviewer plausibility measure corresponding to each of the experiencesummary data elements in the skill profiles of the opportunity seekers;a eleventh computer program code for receiving a rating credibilitymeasure of each of the skills of the opportunity seekers and storingsaid rating credibility measure in the ratings list, wherein the ratingcredibility measure indicates credibility of said rating skill amountmeasure of each of the skills; a twelfth computer program code forgenerating an aggregated experience plausibility measure and theaggregated experience credibility measure corresponding to each of theexperience summary data elements in the experience summary list from thereviewer plausibility measure and the reviewer credibility measurerespectively, corresponding to each of the experience summary dataelements associated with the relationship data stored in therelationship list by computing a weighted credibility measure, anaggregated unadjusted credibility measure, and a credibility bump andstoring the generated aggregated experience plausibility measure and thegenerated aggregated experience credibility measure as the experienceplausibility measure and the experience credibility measurecorresponding to the each of said experience summary data elements inthe experience summary list; and a thirteenth computer program code forgenerating an aggregated skill amount measure and the aggregated skillcredibility measure corresponding to each of the skills of theopportunity seeker in the skills profile list from the rating skillamount measure and the rating credibility measure respectively,corresponding to each of the skills in the ratings list and storing saidgenerated aggregated skill amount measure and the aggregated skillcredibility measure as a skill amount measure and a skill credibilitymeasure corresponding to each of the skills in the skills profile list,wherein the generated aggregated skill credibility measure and thegenerated aggregated experience credibility measure determinecredibility of experience ratings provided by the reviewers onevaluating the experience summary data elements and the skillsassociated with each of the opportunity seekers.

The computer program codes further comprise a fourteenth computerprogram code for receiving profile data of one of the reviewers from theopportunity seeker, determining whether the profile data of thereviewers is available in the user profile list, and stores unavailableprofile data of the reviewers in the user profile list. Thenon-transitory computer readable storage medium, wherein the skill isone of a personal trait and a domain of expertise of an opportunityseeker

FIGS. 23-29 exemplarily illustrate tabular representations fordetermining credibility of experience ratings provided by one or morereviewers, that is, the rating users and discovering skills ofopportunity seekers, that is, the rated users based on a relationshipbetween the reviewers and the opportunity seekers. FIG. 23 exemplarilyillustrates the predefined skill list 302 comprising skills classifiedinto personal traits and domains of expertise. The skills are indicatedby an ISTRAIT flag and the value of the ISTRAIT flag is TRUE for thepersonal traits and the value of the ISTRAIT flag is FALSE for thedomains of expertise. As exemplarily illustrated in FIG. 23 , the skillDEPENDABILITY is a personal trait indicated by TRUE and hypertext markuplanguage (HTML) is a domain of expertise indicated by FALSE.

FIG. 24 exemplarily illustrates the user profile list 301 comprisingprofile data of users of the experience rating and skill discoverysystem (ERSDS) 100, that is, the opportunity seekers and the reviewers.The profile data comprises USER_IDs of the users, first names of theusers, last names of the users, and electronic mail (email) addresses ofthe users. For example, a user is identified with a USER_ID FNERK, FREDas the first name, NERK as the last name, and fnerk@nerkworld.com as theemail address of the user as exemplarily illustrated in FIG. 24 . Theuser FNERK can be an opportunity seeker or a reviewer.

FIG. 25 exemplarily illustrates the skills profile list 303 generated bythe skill profile module 108 a of the experience rating and skilldiscovery system (ERSDS) 100 exemplarily illustrated in FIG. 1 , usingthe profile data in the user profile list 301 exemplarily illustrated inFIG. 24 and the skills in the predefined skill list 302 exemplarilyillustrated in FIG. 23 . The skills profile list 303 comprises skillprofiles. Each skill profile comprises a skill with a correspondingskill profile name SKILL PROFILE NAME, a corresponding skill amountmeasure AMOUNT PRESENT MEASURE, and a corresponding skill credibilitymeasure SKILL CREDIBILITY MEASURE indicating credibility of the skillamount measure. As exemplarily illustrated in FIG. 25 , the skillspossessed by an opportunity seeker with the USER_ID FNERK in a skillprofile with a skill profile name, for example, CLAIM MANAGEMENT, isprovided with NULL values for the skill amount measure and the skillcredibility measure.

FIG. 26 exemplarily illustrates the experience summary list 802 receivedby the user association module 108 b exemplarily illustrated in FIG. 8 .The experience summary list 802 comprises experience summary data setsidentified by the EXP_ID. The experience summary data sets comprise theUSER_ID of each of the opportunity seekers, that is, the rated users inthe skill profiles, skill profile names SKILL PROFILE NAME,corresponding experience plausibility measures, and correspondingexperience credibility measures. Each experience summary data setidentified by the EXP_ID has a start date START_DATE and an end dateEND_DATE. For example, the opportunity seeker with the USER_ID FNERK hasheld a position of a claim management intern at a T.Y.K.E.S ResourceCenter at Chino, Calif. in the past from a start date Jun. 1, 2014 tillDec. 24, 2014 as exemplarily illustrated in FIG. 26 . As a part of theresponsibility in the position of a claim management intern, theopportunity seeker has assessed need and recommended services fordiverse families with children birth to five years of age and has alsoco-led the facilitation of court mandated parenting classes. Theposition held and the responsibilities in the position constitute anexperience summary data element in the experience summary data setidentified by the EXP_ID as 1. As exemplarily illustrated in FIG. 26 ,the experience plausibility measure and the experience credibilitymeasure have NULL values.

FIG. 27 exemplarily illustrates a relationship list 801 comprisingrelationship data, a reviewer plausibility measure, and a reviewercredibility measure of each of the experience summary data sets listedin the experience summary list 802 exemplarily illustrated in FIG. 26 .The relationship data comprises information of the relationshipsidentified by the REL_ID between the reviewers identified by the RATINGUSER_ID and the opportunity seekers identified by the RATED USER_ID, andthe experience summary data element of the experience summary data setidentified by the EXP_ID being evaluated by the reviewer. As exemplarilyillustrated in FIG. 27 , the opportunity seeker identified by FNERK andthe reviewer identified by DFELLA have a relationship identified by theREL_ID as 1 for an experience summary data set identified by the EXP_IDas 2. From the seeker devices 102 and the reviewer devices 103, the userassociation module 108 b and the rating module 108 d exemplarilyillustrated in FIG. 1 collect information about the relationship andstore the collected information in the relationship list 801. The userassociation module 108 b and the rating module 108 d configure NULLvalues for the reviewer plausibility measure and the relationship depthrespectively, corresponding to the relationship identified by the REL_IDas 1.

FIG. 28 exemplarily illustrates a ratings list 803 comprising ratingsand relationship depths corresponding to the skills, generated by theuser association module 108 b exemplarily illustrated in FIG. 8 , of theexperience rating and skill discovery system (ERSDS) 100. An opportunityseeker, that is, a rating user in a relationship identified by theREL_ID with a reviewer, that is, a rated user associated with anexperience summary data element in the experience summary data setidentified by the EXP_ID awards a rating skill amount measureAMOUNT_PRESENT and a strength of belief measure STRENGTH OF BELIEF foreach of the o skills associated with the experience summary dataelement. The relationship measurement module 239 exemplarily illustratedin FIG. 15 , computes the relationship depth RELATIONSHIP DEPTHcorresponding to each of the skills possessed by the opportunity seekerand the rating module 108 d exemplarily illustrated in FIG. 1 , andstores the relationship depth in the ratings list 803. In an example, ina relationship identified by the REL_ID as 1, the reviewer identified byDFELLA rates the opportunity seeker identified by FNERK for each of theskills such as tutoring, Microsoft® Word, etc., possessed by FNERK inthe experience summary data element of the experience summary data setidentified by the EXP_ID as 2, as exemplarily illustrated in FIG. 28 .The rating module 108 d configures NULL values for the rating skillamount measure and the strength of belief measure. The reviewer DFELLAprovides the rating skill amount measure and the strength of beliefmeasure from the reviewer device. The relationship measurement module239 computes the relationship depth of the relationship between theopportunity seeker and the reviewer with regard to the skill. Thecredibility module 240 exemplarily illustrated in FIG. 16 , computes therating credibility measure and the rating module 108 d stores thecomputed rating credibility measure corresponding to skills possessed byFNERK in the experience summary data element of the experience summarydata set identified by the EXP_ID as 2 in the ratings list 803.

For the N reviewer credibility measures received from the credibilitymodule 240 in the relationship list 801 exemplarily illustrated in FIG.27 , the aggregation module 108 e generates an aggregated experienceplausibility measure and an aggregated experience credibility measurefrom the reviewer plausibility measures and the reviewer credibilitymeasures respectively, per rating user using the measure aggregator 246exemplarily illustrated in FIGS. 18-19 and FIG. 21 . For the N ratingscomprising rating credibility measures received from the credibilitymodule 240 in the ratings list 803 exemplarily illustrated in FIG. 28 ,the aggregation module 108 e using the measure aggregator 246 generatesan aggregated skill amount measure and an aggregated skill credibilitymeasure entered per rating user for each of the skills. The aggregationmodule 108 e invokes the measure aggregator 246 twice. The aggregationmodule 108 e updates the aggregated experience plausibility measure andthe aggregated experience credibility measure as the experienceplausibility measure and the experience credibility measurerespectively, in the experience summary list 802 exemplarily illustratedin FIG. 26 . The aggregation module 108 e further updates the aggregatedskill amount measure and the aggregated skill credibility measure as theskill amount measure and the skill credibility measure respectively,corresponding to each of the skills in the skills profile list 303exemplarily illustrated in FIG. 25 .

FIG. 29 exemplarily illustrates a user profile list 301 comprisingprofile data of at least one user, that is, an opportunity seeker or areviewer, stored in a database of the experience rating and skilldiscovery system (ERSDS) 100. The user profile list 301 is populated bythe skill profile module 108 a exemplarily illustrated in FIG. 1 , asthe skill profile module 108 a receives first names, last names, andelectronic mail addresses of the users from the seeker devices and thereviewer devices.

FIG. 30 exemplarily illustrates the experience summary list 802comprising at least one experience summary data set received from theseeker devices 102 of the opportunity seekers, stored in a database ofthe experience rating and skill discovery system (ERSDS) 100. The userassociation module 108 b exemplarily illustrated in FIG. 1 , receivesthe experience summary list 802 comprising experience summary dataelements received from the opportunity seekers via the seeker devices102.

The foregoing examples have been provided merely for explanation and arein no way to be construed as limiting of the experience rating and skilldiscovery system (ERSDS) 100 and the method disclosed herein. While theERSDS 100 and the method have been described with reference to variousembodiments, it is understood that the words, which have been usedherein, are words of description and illustration, rather than words oflimitation. Furthermore, although the ERSDS 100 and the method have beendescribed herein with reference to particular means, materials, andembodiments, the ERSDS 100 and the method are not intended to be limitedto the particulars disclosed herein; rather, the ERSDS 100 and themethod extend to all functionally equivalent structures, methods anduses, such as are within the scope of the appended claims. Whilemultiple embodiments are disclosed, it will be understood by thoseskilled in the art, having the benefit of the teachings of thisspecification, that the ERSDS 100 and the method disclosed herein arecapable of modifications and other embodiments may be effected andchanges may be made thereto, without departing from the scope and spiritof the ERSDS 100 and the method disclosed herein.

We claim:
 1. An experience rating and skill discovery system comprising:at least one web computer server rendering a graphical user interface ona plurality of seeker devices and reviewer devices; at least onedatabase server communicatively coupled to said at least one webcomputer server via a network, said at least one database server hostingone or more databases for storing a user profile list, a generatedskills profile list, a predefined skill list, an experience summarylist, a relationship list, and a ratings list; and at least oneprocessing computer server comprising at least one processorcommunicatively coupled to said at least one web computer server, saidat least one database server, said plurality of seeker devices, and saidreviewer devices via said network, said at least one processorconfigured to execute computer program instructions defined by modulesof said experience rating and skill discovery system, said modules ofsaid experience rating and skill discovery system comprising: a skillprofile module for reading profile data of opportunity seekers stored insaid user profile list; said skill profile module for generating askills profile list comprising skill profiles associated with saidopportunity seekers, using stored profile data and skills selected bysaid opportunity seekers from said predefined skill list via saidplurality of seeker devices; a user association module for receivingsaid experience summary list comprising experience summary data elementsin said skill profiles associated with said opportunity seekers withcorresponding experience plausibility measures and correspondingexperience credibility measures, from said at least one database server;an invitation module for transmitting invitations to said reviewerdevices for evaluating said experience summary data elements in saidexperience summary list and said skills in said generated skills profilelist; said user association module for determining whether profile dataof each of one or more reviewers with said transmitted invitations isavailable in said user profile list; said user association module forconfiguring in said relationship list a reviewer plausibility measurecorresponding to each of said experience summary data elements in saidskill profiles of said opportunity seekers; said user association modulefor collecting relationship data comprising information of relationshipsbetween each of said one or more reviewers and each of said opportunityseekers and storing said collected relationship data in saidrelationship list; said user association module for generating saidratings list, wherein ratings in said ratings list comprise a ratingskill amount measure and a strength of belief measure for each of saidskills, and configuring said rating skill amount measure and saidstrength of belief measure for each of said skills in said ratings listto NULL; said rating module for receiving said reviewer plausibilitymeasure corresponding to each of said experience summary data elementsin said skill profiles of said opportunity seekers from said reviewerdevices and updating in said relationship list said received reviewerplausibility measure corresponding to each of said experience summarydata elements in said skill profiles of said opportunity seekers; saidrating module for receiving said ratings provided by said each of saidone or more reviewers from said reviewer devices on evaluating saidskills associated with said experience summary data elements in saidskill profiles associated with said opportunity seekers, and updatingsaid ratings list based on said ratings provided by said each of saidone or more reviewers; said rating module for receiving a reviewercredibility measure of said each of said experience summary dataelements in said skill profiles associated with said opportunityseekers, from a credibility module of said external operational system,and storing said reviewer credibility measure in said updatedrelationship list; said rating module for receiving a rating credibilitymeasure of said each of said skills from said credibility module, andstoring said rating credibility measure in said updated ratings list; anaggregation module for generating an aggregated experience plausibilitymeasure and an aggregated experience credibility measure correspondingto said each of said experience summary data elements in said experiencesummary list, from said updated reviewer plausibility measure and saidreviewer credibility measure respectively, corresponding to said each ofsaid experience summary data elements associated with said relationshipdata stored in said updated relationship list; and said aggregationmodule for generating an aggregated skill amount measure and anaggregated skill credibility measure corresponding to said each of saidskills in said generated skills profile list, using said rating skillamount measure and said rating credibility measure respectively,corresponding to said each of said skills in said updated ratings list.2. The experience rating and skill discovery system of claim 1, whereinsaid skill profiles associated with said opportunity seekers comprisesskills associated with said opportunity seekers with corresponding skillamount measures and corresponding skill credibility measures indicatingcredibility of said skill amount measures, wherein said skillsassociated with said opportunity seekers comprise one of a personaltrait and a domain of expertise of said opportunity seekers, and whereinsaid transmitting said invitations to said reviewer devices allows oneor more reviewers to discover said skills associated with saidopportunity seekers in a skill profile associated with said experiencesummary data elements in said experience summary list.
 3. The experiencerating and skill discovery system of claim 1, wherein said rating moduleupdates said ratings list comprising a computed relationship depthcorresponding to each of said skills, wherein said ratings comprise saidrating skill amount measure and said strength of belief measure for eachof said skills, and wherein said computed relationship depth computed bya relationship measurement module of an external operational system is afactor in computing said aggregated experience credibility measurecorresponding to said each of said experience summary data elements insaid experience summary list and said aggregated skill credibilitymeasure corresponding to said each of said skills in said generatedskills profile list.
 4. The experience rating and skill discovery systemof claim 1, wherein said reviewer credibility measure computed by saidcredibility module of said external operational system is indicative ofcredibility of said reviewer plausibility measure corresponding to saideach of said experience summary data elements in said skill profilesassociated with said opportunity seekers, and wherein said ratingcredibility measure computed by said credibility module indicatescredibility of said rating skill amount measure of said each of saidskills.
 5. The experience rating and skill discovery system of claim 1,further comprising: said aggregation module generating said aggregatedexperience plausibility measure and said aggregated experiencecredibility measure corresponding to said each of said experiencesummary data elements in said experience summary list by computing aweighted credibility measure, an aggregated unadjusted credibilitymeasure, and a credibility bump, and storing said generated aggregatedexperience plausibility measure and said generated aggregated experiencecredibility measure as said experience plausibility measure and saidexperience credibility measure corresponding to said each of saidexperience summary data elements in said experience summary list; andsaid aggregation module storing said generated aggregated skill amountmeasure and said aggregated skill credibility measure as a skill amountmeasure and a skill credibility measure corresponding to said each ofsaid skills in said generated skills profile list, and wherein saidgenerated aggregated skill credibility measure and said generatedaggregated experience credibility measure determine credibility ofexperience ratings provided by said one or more reviewers on evaluatingsaid experience summary data elements and said skills associated withsaid opportunity seekers.
 6. The experience rating and skill discoverysystem of claim 1, wherein said user association module receives andstores profile data of said one or more reviewers with said transmittedinvitations from said opportunity seeker when said profile data of saidone or more reviewers is unavailable in said user profile list.
 7. Amethod employing an experience rating and skill discovery systemcomprising at least one processor, said method comprising: readingprofile data of opportunity seekers stored in a user profile list bysaid experience rating and skill discovery system; generating a skillsprofile list comprising skill profiles associated with said opportunityseekers, using stored profile data and skills selected by saidopportunity seekers from a predefined skill list via a plurality ofseeker devices; receiving an experience summary list comprisingexperience summary data elements in said skill profiles associated withsaid opportunity seekers with corresponding experience plausibilitymeasures and corresponding experience credibility measures, by saidexperience rating and skill discovery system, from at least one databaseserver; transmitting invitations to a plurality of reviewer devices forevaluating said experience summary data elements in said experiencesummary list and said skills in said generated skills profile list, bysaid experience rating and skill discovery system; determining whetherprofile data of one or more reviewers with said transmitted invitationsis available in said user profile list, by said experience rating andskill discovery system; configuring in a relationship list a reviewerplausibility measure corresponding to each of said experience summarydata elements in said skill profiles associated with said opportunityseekers, by said experience rating and skill discovery system;collecting relationship data comprising information of relationshipsbetween each of said one or more reviewers and each of said opportunityseekers, and storing said collected relationship data in saidrelationship list, by said experience rating and skill discovery system;generating a ratings list, by said experience rating and skill discoverysystem, wherein ratings in said ratings list comprise a rating skillamount measure and a strength of belief measure for each of said skills,and configuring said rating skill amount measure and said strength ofbelief measure for each of said skills in said ratings list to NULL;receiving said reviewer plausibility measure corresponding to each ofsaid experience summary data elements in said skill profiles of saidopportunity seekers from said reviewer devices and updating in saidrelationship list said received reviewer plausibility measurecorresponding to each of said experience summary data elements in saidskill profiles of said opportunity seekers, by said experience ratingand skill discovery system; receiving ratings provided by said each ofsaid one or more reviewers from said reviewer devices on evaluating saidskills associated with said experience summary data elements in saidskill profiles associated with said opportunity seekers, and updatingsaid ratings list based on said ratings provided by said each of saidone or more reviewers; receiving a reviewer credibility measure of saideach of said experience summary data elements in said skill profilesassociated with said opportunity seekers, and storing said reviewercredibility measure in said updated relationship list, by saidexperience rating and skill discovery system; receiving a ratingcredibility measure of said each of said skills, and storing said ratingcredibility measure in said updated ratings list, by said experiencerating and skill discovery system; generating an aggregated experienceplausibility measure and an aggregated experience credibility measurecorresponding to said each of said experience summary data elements insaid experience summary list, from said updated reviewer plausibilitymeasure and said reviewer credibility measure respectively, by saidexperience rating and skill discovery system, corresponding to said eachof said experience summary data elements associated with saidrelationship data stored in said updated relationship list; andgenerating an aggregated skill amount measure and an aggregated skillcredibility measure corresponding to said each of said skills in saidgenerated skills profile list, from said rating skill amount measure andsaid rating credibility measure respectively, by said experience ratingand skill discovery system, corresponding to said each of said skills insaid updated ratings list.
 8. The method of claim 7, wherein said skillprofiles associated with said opportunity seekers comprise skillsassociated with said opportunity seekers with corresponding skill amountmeasures and corresponding skill credibility measures indicatingcredibility of said skill amount measures, wherein said skillsassociated with said opportunity seekers comprise one of a personaltrait and a domain of expertise of said opportunity seekers, and whereinsaid transmitting said invitations to said reviewer devices allows oneor more reviewers to discover said skills associated with saidopportunity seekers in a skill profile associated with said experiencesummary data elements in said experience summary list.
 9. The method ofclaim 7, further comprising updating said ratings list comprising acomputed relationship depth corresponding to each of said skills, bysaid experience rating and skill discovery system, wherein said ratingscomprise said rating skill amount measure and said strength of beliefmeasure for each of said skills, and wherein said computed relationshipdepth computed by a relationship measurement module of an externaloperational system is a factor in computing said aggregated experiencecredibility measure corresponding to said each of said experiencesummary data elements in said experience summary list and saidaggregated skill credibility measure corresponding to said each of saidskills in said generated skills profile list.
 10. The method of claim 7,wherein said reviewer credibility measure computed by said credibilitymodule of said external operational system is indicative of credibilityof said reviewer plausibility measure corresponding to said each of saidexperience summary data elements in said skill profiles associated withsaid opportunity seekers, and wherein said rating credibility measureindicates credibility of said rating skill amount measure of said eachof said skills.
 11. The method of claim 7, further comprising:generating said aggregated experience plausibility measure and saidaggregated experience credibility measure corresponding to said each ofsaid experience summary data elements in said experience summary list,by said experience rating and skill discovery system, by computing aweighted credibility measure, an aggregated unadjusted credibilitymeasure, and a credibility bump, and storing said generated aggregatedexperience plausibility measure and said generated aggregated experiencecredibility measure as said experience plausibility measure and saidexperience credibility measure corresponding to said each of saidexperience summary data elements in said experience summary list; andstoring said generated aggregated skill amount measure and saidaggregated skill credibility measure as a skill amount measure and askill credibility measure corresponding to said each of said skills insaid generated skills profile list, by said experience rating and skilldiscovery system, wherein said generated aggregated skill credibilitymeasure and said generated aggregated experience credibility measuredetermine credibility of experience ratings provided by said one or morereviewers on evaluating said experience summary data elements and saidskills associated with said opportunity seekers.
 12. The method of claim7, further comprising receiving and storing profile data of said one ormore reviewers with said transmitted invitations from said opportunityseeker when said profile data of said one or more reviewers isunavailable in said user profile list, by said experience rating andskill discovery system.
 13. A non-transitory computer readable storagemedium having embodied thereon, computer program codes comprisinginstructions executable by at least one processor, said computer programcodes comprising: a first computer program code for reading profile dataof opportunity seekers stored in a user profile list; a second computerprogram code for generating a skills profile list comprising skillprofiles associated with said opportunity seekers using said profiledata and skills selected by said opportunity seekers from a predefinedskill list via a plurality of seeker devices, wherein said skillprofiles associated with said opportunity seekers comprise skillsassociated with said opportunity seekers with corresponding skill amountmeasures and corresponding skill credibility measures indicatingcredibility of said skill amount measures; a third computer program codefor receiving an experience summary list comprising experience summarydata elements in said skill profiles associated with said opportunityseekers with corresponding experience plausibility measures andcorresponding experience credibility measures, from at least onedatabase server; a fourth computer program code for transmittinginvitations to said reviewer devices for evaluating said experiencesummary data elements in said experience summary list and said skills insaid generated skills profile list; a fifth computer program code fordetermining whether profile data of each of one or more reviewers withsaid transmitted invitations is available in said user profile list; asixth computer program code for configuring in a relationship list areviewer plausibility measure corresponding to each of said experiencesummary data elements in said skill profiles of said opportunityseekers; a seventh computer program code for collecting relationshipdata comprising information of relationships between each of said one ormore reviewers and each of said opportunity seekers and storing saidcollected relationship data in said relationship list; an eighthcomputer program code for generating a ratings list, wherein ratings insaid ratings comprise a rating skill amount measure and a strength ofbelief measure for each of said skills, and configuring said ratingskill amount measure and said strength of belief measure for each ofsaid skills in said ratings list to NULL; a ninth computer program codefor receiving said reviewer plausibility measure corresponding to eachof said experience summary data elements in said skill profiles of saidopportunity seekers from said reviewer devices and updating in saidrelationship list said received reviewer plausibility measurecorresponding to each of said experience summary data elements in saidskill profiles of said opportunity seekers; a tenth computer programcode for receiving said ratings provided by said each of said one ormore reviewers from said reviewer devices on evaluating said skillsassociated with said experience summary data elements in said skillprofiles associated with said opportunity seekers, and updating saidratings list based on said ratings provided by said each of said one ormore reviewers; an eleventh computer program code for receiving areviewer credibility measure of said each of said experience summarydata elements in said skill profiles associated with said opportunityseekers, from a credibility module of said external operational system,and storing said reviewer credibility measure in said updatedrelationship list; a twelfth computer program code for ratingcredibility measure of said each of said skills from said credibilitymodule, and storing said rating credibility measure in said updatedratings list; a thirteenth computer program code for generating anaggregated experience plausibility measure and an aggregated experiencecredibility measure corresponding to said each of said experiencesummary data elements in said experience summary list, from said updatedreviewer plausibility measure and said reviewer credibility measurerespectively, corresponding to said each of said experience summary dataelements associated with said relationship data stored in said updatedrelationship list; and a fourteenth computer program code for generatingan aggregated skill amount measure and an aggregated skill credibilitymeasure corresponding to said each of said skills in said generatedskills profile list, using said rating skill amount measure and saidrating credibility measure respectively, corresponding to said each ofsaid skills in said updated ratings list.
 14. The non-transitorycomputer readable storage medium of claim 13, wherein said skillprofiles associated with said opportunity seekers comprises skillsassociated with said opportunity seekers with corresponding skill amountmeasures and corresponding skill credibility measures indicatingcredibility of said skill amount measures, wherein said skillsassociated with said opportunity seekers comprise one of a personaltrait and a domain of expertise of said opportunity seekers, and whereinsaid transmitting said invitations to said reviewer devices allows oneor more reviewers to discover said skills associated with saidopportunity seekers in a skill profile associated with said experiencesummary data elements in said experience summary list.
 15. Thenon-transitory computer readable storage medium of claim 13, furthercomprising a fifteenth computer program code for updating said ratingslist comprising a computed relationship depth corresponding to each ofsaid skills, wherein said ratings comprise said rating skill amountmeasure and said strength of belief measure for each of said skills, andwherein said computed relationship depth computed by a relationshipmeasurement module of an external operational system is a factor incomputing said aggregated experience credibility measure correspondingto said each of said experience summary data elements in said experiencesummary list and said aggregated skill credibility measure correspondingto said each of said skills in said generated skills profile list. 16.The non-transitory computer readable storage medium of claim 13, whereinsaid reviewer credibility measure computed by said credibility module ofsaid external operational system is indicative of credibility of saidreviewer plausibility measure corresponding to said each of saidexperience summary data elements in said skill profiles associated withsaid opportunity seekers, and wherein said rating credibility measurecomputed by said credibility module indicates credibility of said ratingskill amount measure of said each of said skills.
 17. The non-transitorycomputer readable storage medium of claim 13, further comprising: asixteenth computer program code for generating said aggregatedexperience plausibility measure and said aggregated experiencecredibility measure corresponding to said each of said experiencesummary data elements in said experience summary list by computing aweighted credibility measure, an aggregated unadjusted credibilitymeasure, and a credibility bump, and storing said generated aggregatedexperience plausibility measure and said generated aggregated experiencecredibility measure as said experience plausibility measure and saidexperience credibility measure corresponding to said each of saidexperience summary data elements in said experience summary list; and aseventeenth computer program code for storing said generated aggregatedskill amount measure and said aggregated skill credibility measure as askill amount measure and a skill credibility measure corresponding tosaid each of said skills in said generated skills profile list, whereinsaid generated aggregated skill credibility measure and said generatedaggregated experience credibility measure determine credibility ofexperience ratings provided by said one or more reviewers on evaluatingsaid experience summary data elements and said skills associated withsaid opportunity seekers.
 18. The non-transitory computer readablestorage medium of claim 13, further comprising an eighteenth computerprogram code for receiving and storing said profile data of said one ormore reviewers with said transmitted invitations from said opportunityseeker when said profile data of said one or more reviewers isunavailable in said user profile list.